Business Analytics (90+ New Business Hacks 2019)

Business Analytics

90+ Business Analytics Hacks 2019

We have moved from being an analog world to a very digital one. E-commerce, newspapers, the Internet of things (IoT), music streaming— all generate huge amounts of data.


The art of combining every data footprint and gaining actionable insights from that huge cache of data, which helps businesses grow, improves customer experience, and generates revenue, is known as business analytics. This blog explains the 90+ new business analytics hacks that will used in 2019.


Business analytics is about providing insights, about driving businesses intelligently— and in a fact-driven manner. Previously, data resided in silos, and it was next to impossible to combine sales data with inventory data in an automated way.


It required many manual adjustments, resulting in a long, tedious process. But with the advent of business analytics, data integration between different systems is relatively easier and provides a holistic view of an enterprise organization’s data.


The process of gathering, combining, exploring, storing, predicting, and utilizing data, and the attendant requirements of a robust IT infrastructure to integrate disparate systems such as order management systems, ERP, CRM, billing, customer care, market research data, etc., to derive data-driven conclusions, is known as business analytics.


The intelligence generated from data adds value to businesses, by generating leads, retaining customers, increasing revenue generation, gaining competitive advantage, and improving business operations. The data from social media helps businesses understand their customers better.


By listening to what their customers have to say about the products and services they offer, businesses can determine if they are losing customers, owing to their competitors’ products fulfilling the consumers’ needs in a much better way.


Market analysis data helps organizations to identify new consumer segments that need to be tapped, unexplored markets, potentially lucrative geographical segments, and spending trends of consumers and consumer-response trends regarding different campaigns.


The inventory, web, and other device’s log data helps to promote and improve inventory management, data security, fraud detection, IT operations, and online response times, which, in turn, result in better utilization of resources, improved consumer experiences, and innovative business operations.


Business analytics is paramount for organizations wanting to keep abreast of the digital revolution that has taken the world by storm. Only those organizations that invest in business analytics will lead the pack, as today’s consumers demand personalized attention, while, at the same time, their attention span is very short, and there are many competitors vying for the same market.


The different processes that analytics encompass are as follows:

  • Data storage
  • Data integration
  • Data analysis
  • Data mining
  • Predictive analytics
  • Measuring campaign effectiveness
  • Measuring online behavior through web analytics
  • Data science
  • Master data management
  • Data security and compliance policies
  • IoT


To distinguish the abundance of data from every digital touch point, terms such as big data and data science were coined. But analytics existed way before big data came into the picture.


Earlier business analytics was known as reporting, dashboard, MI (Management Information) reporting, decision support systems, data warehouses, and the like, but it was and is basically data presented in a manner that makes it easy for users to arrive at decisions and predict outcomes.


Big data and business analytics are being used synonymously, but analytics can be implemented irrespective of the amount of data. Any business that has the need for improvement—be it in terms of reducing cost, increasing data monetization, enhancing business processes— should start capturing and analyzing data from relevant systems, in other words, turn to business analytics!


The applications of analytics are manifold, not merely to web analytics or financial data analysis. Analytics is being used to save human lives, by analyzing data from wearable devices that measure vital body statistics. This data is also used to further research and development of medicines.


Analytics is being used in smart homes, providing centralized control of heating, ventilation, and air conditioning (HVAC) systems. The data from such systems is analyzed to optimize power utilization.


In the retail field, data about customer purchase history is being used to send out personalized recommendations to customers. Travel companies thrive on good customer reviews, to attract more customers, and data from social media sites has become very important in gauging customer sentiments about products and services.


With our increased online presence, we leave a lot of digital footprints all over the Internet—when we purchase things, stream movies and music, or participate in online courses or social media. More and more companies have begun analyzing that digital footprint data to gain insights into their business.


The more companies know about their customers, the more they can personalize the products and services they offer to their customers. It is not only the data generated by the businesses that are interesting but also that regarding customers’ likes and dislikes that can be gathered from social media platforms and business partner sites.


But because analytics is about data, data security and protection laws should be abided by. In short, analytics is a mélange of several processes like the ones named. Analytics requires integrating several systems to enable a holistic view of the customer lifecycle.


Analytics requires data storage management, data security compliance systems, predictive modeling functionalities, and neat and interactive data visualization abilities.


Implications of Business Analytics

Business analytics provides fact-based actionable insights about business data. Fact-based because the data is real, collected from the business processes, and customer interactions. Common examples of data-driven insights are

  • Demographics of customers, by segmentation
  • The popularity of products bought by customers, by analyzing sales data
  • Inventory management, by querying inventory systems
  • Online behavior, by analyzing web analytics data
  • Targeting customers for revenue generation, based on their purchase history, by analyzing historical sales data
  • Product recommendation to customers, based on their previous online behavior, by using predictive analytics


In order to be able to gain the above insights, many technical systems have to be integrated for data mining. Data analysis requires data from order management to web analytics to CMS (Content Management System) data, some of it is required to be analyzed in real time.


Business analytics implementations not only generate revenue and let organizations know their customer needs better but also require that organizations have clear strategies, appropriate IT systems that are easy to integrate, and control over their products and processes.


Defining business rules, processes, and products at an enterprise level that can be used as reference data is known as master data management, thereby avoiding confusion.


Many organizations that have a global presence use different nomenclature for the same processes and products. Implementing both a local and a global definition for terms and mapping them to each other is a good practice.


For example, in an enterprise organization, payment methods can have both global and local definitions. At a global level, payment methods might be broadly classified as

  • Web payment
  • Mobile payment
  • Credit card
  • Bank transfer
  • Phone pay
  • SMS (Short Message Service)
  • Company agreement
  • Web payment platform


But local definitions could be country-specific, such as in the case of Swish, a payment method used exclusively in Sweden. But it could be mapped to a bank transfer. Maintaining the data at both global and local levels allows flexibility and analysis of data at different levels.


If, for example, a business requirement is to follow up on the total number of bank transfers globally, this also can be achieved locally. For example, the number of bank transfers using Swish in Sweden can be also analyzed globally.


Business analytics, although an IT process, is mainly for the business side, so it should be an area of shared competence between IT and business, with typical teams consisting of data architects, data scientists, business intelligence developers, and business analysts who liaise with marketing and sales teams and business controllers or with chief digital officers and other management staff, based on the business case.


Most organizations, even today, have separate business analytics and business divisions, which communicate to each other only when the need arises for a business requirement that transcends both business and IT.


Traditionally, business analysts have analyzed functional requirements to translate these to technical specifications, while data analysts are more technical, gathering, cleansing, and analyzing data.


To increase the analytics throughput of a company, it is vital to combine the business and analytics competencies, to be able to analyze the data from a business perspective, so as to draw conclusions about consumer behavior, find trends, and, accordingly, implement business decisions with targeted marketing campaigns.


It is, therefore, good to have business analysts in the marketing and sales divisions, or to train sales and marketing managers in analytics, so that they are able to analyze data themselves.


There are many self-service business analytics tools available that require little or no IT intervention and allow nontechnical business users to analyze and slice and dice data in order to gather insights.


Successful businesses are the ones that consistently achieve their KPIs (key performance indicators). KPIs are a way of measuring results for a business, by analyzing the main business drivers.


The main business drivers are not only revenue-generation channels but also business process optimization, cost reduction, optimal resource utilization, and an effective product management lifecycle.


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KPIs can be defined by

  • Pre-defined business processes, revenue margins, and product lifecycle milestones
  • Strategic business process optimization, cost-effectiveness, and product launches guided by quantitative measurement methods
  • Analyzing and comparing the achieved goals and revenue against the set goals


Having the right business analytics strategy, based on business goals, is of significant importance. Once the business goals and KPIs are clearly defined, a business analytics strategy can be put in place, suited to the business requirements and the need for data analysis.


Every organization has unique requirements and, therefore, its analytics strategy, which takes into account data storage methodologies, the means of data access—such as APIs (Application Programming Interface) or ETL (extraction, transformation, and load), data-visualization tools, and latency and data redundancy to handle exceptions and load balancing, has to be designed according to the business requirements of the organization.


Challenges Faced

Owing to business analytics being able to generate quantifiable facts that provide organizations with a foundation for informed decision making, it is not too much trouble to gain buy-ins from stakeholders or senior management for a business analytics business case.


If stakeholders are presented with an analytics blueprint that ensures a return on investment (ROI) that results in a considerable business gain, the stakeholders should be willing to invest in the business case. The challenges, however, arise when the actual implementation of an analytics strategy takes place without prior clear roadmaps having been defined.


Some of the challenges faced in business analytics are as follows:


  • Data integration and too many source systems: Deriving conclusions that cross over different technological platforms can be challenging. It is advantageous, then, to choose source systems that can be easily integrated into each other and the analytics platform.


  • Data quality issues: Owing to different source systems maintaining data in different formats, often there are data-quality issues when integrating data from different sources. While it is difficult, organizations should strive to have more standardized data in source systems.


  • Data storage due to huge amounts of data: With the ever-increasing amount of data, storage becomes an issue. With so many data-storage options available today—cloud, on-premise, appliances, and distributed file systems—organizations should choose storage systems taking into account their business requirements and how rapidly their data increases.


  • In some industries such as media, in which it is important to track every mouse click to analyze the traffic, the amount of data that needs to be stored to generate insights is huge. There needs to be a mechanism to handle the storage of data—some operational, some real-time.


Response time for data querying: Querying data may lead to latency if the amount of data being retrieved is substantial and complex.


  • Long implementation time periods: Business analytics projects may have long implementation durations, depending on all the aforementioned factors, thereby misleading the stakeholders in their belief that business analytics yields faster results. Iterative processes that deliver value in stages is the right approach.


  • Lack of data governance and metadata management: This is a very common problem in organizations, as the business sides are not very aware of technical complications that arise due to lack of routines, business definitions, and data-access rights. IT departments can implement the business requirements, but the requirements and definitions have to be defined by the business.


  • Any organization has a defined set of business rules and entities defined by the business, but definitions may vary if there are too many departments involved, or if there are many teams spread out geographically. It is imperative to define business entities and rules at an enterprise level and communicate the same to all departments involved.


  • Lack of knowledge and poor communication between business and IT: While IT can certainly come up with technical solutions, the business requirements are handled by the stakeholders, taking into account organizational needs.


  • But this could result in a tug-of-war between IT and business if the responsibilities are not made clear. It is critical that business and IT have a high-level understanding of business alignment and the dependency on each other.


  • No clear strategy in place, hence no clarity on KPIs that drive business: If there are no clear KPIs defined, it is a hindrance to analytics implementations, as that implies no clarity regarding quantifying goals. If it is not clear what needs to be measured, then the analytics project goals cannot be defined.


While the technical issues can be solved to a large extent as data storage gets cheaper and innovative business analytics tools make data integration easier and cut short implementation durations, it is of utmost importance that data strategies and KPIs that define business goals be in place before any analytics initiative is undertaken.


  • The C-level executives may have in mind a different set of KPIs that drive the overall business, while the middle management may be interested in KPIs that give them insights about the efficiency of certain business operations.


  • It is critical, therefore, that the analytics implementation takes into account the expectations of every team that expects a result and that it delivers accordingly.



Business analytics is a multidisciplinary investment, transcending IT and various business departments and involving everyone from top management to the developers who deliver the solutions, thereby creating an atmosphere that encourages knowledge exchange, cooperation, and data-driven processes.


  • This coordination is a side effect of analytics initiatives found across organizations. Measuring, optimizing, and analyzing the effects is what analytics is all about, but, to generate value, it entails overcoming lots of business as well as technical complexities.


  • But when the data chain does deliver great business insights and value, it becomes worth the challenge. The advantages of analytics implementations far outweigh the challenges.


SAP Business Analytics Suite of Products

The previous section discussed the emerging market for business analytics, with data being used to gain insights into every aspect of businesses and its growing importance in organizations. Businesses that take advantage of business analytics capabilities have an edge with regard to business process optimization and revenue generation.


With the importance of extracting, integrating, storing, and analyzing data for business advancement being at an all-time high, owing to every other organization turning to data for actionable insights, there is a huge market for analytics products.


There are myriad solutions available in the market that suit the different needs of gathering, storing, analyzing, and visualizing data. The crux, however, lies in finding the right fit for the job. The key features of looking for the right business analytics product for any given organization are as follows:


Ease of integration with other sources:

Do not forget the legacy systems that have to be integrated. The different versions of software updates required, and the need to have other software and operating systems updated to be in sync with them, make certain products cumbersome.


If a product requires frequent updating, it is probably unstable, and the maintenance cost may often outweigh the benefit of some flashy features that it may possess.


Features and functions that suit the requirements of the organization in question: A business analytics product could have the best-in-breed features but may lack some particular features that the organization in question requires. In that case, it does not fulfill the business needs.


Is, preferably, full stack:

Because an analytics solution requires several layers of product stacks, such as databases, ETL, and reporting and dashboarding tools, having a full-stack solution would mean having the same software vendor’s products for each of the layers.


This certainly makes it easier to have different modules of the same product suite, as this eases the integrations between different products, making licensing easier and probably cheaper.


Promotes self-service:

Business requirement implementations entail IT support and resources for implementation. Empowering business users with self-service products increases organizational effectiveness by reducing the time spent going through several business processes to implement new requirements.


In order to implement self-service, the data access and the technicalities involved must be simplified to an extent that allows nontechnical business users to work with minimum technical dependency.


Is mobile:

With ever-increasing mobile usage, the need of the hour is to have business analytics products that are mobile-adapted. To be able to access data anywhere, anytime, online or offline, is essential for business users, for effective and quick decision making.


Takes a shorter time to market:

Business requirements change quickly and, therefore, require tools and processes that yield results fast. If the implementation time for a business analytics tool is too long, the business requirements may become obsolete by the time the implementation phase is completed.


In a world of highly demanding business requirements, a business analytics tool that has a short implementation time is key to serving an organization’s analytics needs.


Has a reasonable licensing cost:

While choosing an analytics product, licensing costs are an important factor to consider. Usually, product vendors have different pricing models, each having pros and cons.


Organizations should weigh these pros and cons and decide which licensing model suits their requirements best, based on the number of server installations, the number of developer licenses, and the number of end-user licenses required.


Offers scalability and flexibility:

A product that is scalable is one that is able to handle huge amounts of data, add more users, migrate existing systems, and has backward compatibility. The amount of data that is gathered in today’s businesses is enormous and ever-increasing; therefore, the analytics tool chosen should be able to scale the high demands of high-performance organizations.


That said, SAP Business Analytics has a full suite of products covering every aspect of analytics, from data integration to storage, analyzing data in real time, predicting patterns, and visualization.


The need to store and analyze data arises from the need to make information available at all levels within an organization, providing easy access, which is a unique selling point (USP) of SAP Business Analytics’ suite of products.


SAP Business Analytics is meant for enterprise organizations with large-scale information distribution. SAP Business Analytics has such data extraction tools as BusinessObjects Data Services and storage in in-memory databases such as HANA and includes a number of products for visualization, such as

  • SAP BusinessObjects Business Intelligence
  • SAP BusinessObjects Dashboards
  • SAP BusinessObjects Design Studio
  • SAP BusinessObjects Lumira
  • SAP BusinessObjects Explorer
  • SAP BusinessObjects Crystal Reports
  • SAP BusinessObjects Analysis
  • SAP Predictive Analytics


Capabilities of SAP Analytics

The capabilities of SAP Business Analytics are manifold, but mainly it distributes data within organizations at all levels, irrespective of the size of the organization, promoting faster time to market and ease of use.


SAP Business Analytics shields the business side from behind-the-scene technical complexities involved in integrating, storing, and analyzing data, making it easier for a business to focus on effective decision making.


Some key capabilities of SAP Business Analytics are


Improved business user autonomy: Business users are empowered by being able to access data with greater ease and by faster, mostly by drag-and-drop, functions.


Better data governance abilities: SAP Analytics products have data-level and user-level access rights management in place to implement robust data governance.


Faster time to market:

SAP Analytics products are quicker to implement, as SAP understands that businesses are very demanding, and if solutions have a very long implementation cycle, they become obsolete.


Integration with existing IT infrastructure:

SAP Analytics has many plug-ins available to integrate with both SAP and non-SAP third-party products. The analytics visualization is compatible with databases such as Oracle, SQL Server, and Sybase or connected to HANA or Hadoop.


Organizations that plan to implement SAP Analytics do not have to change data storage in order to be SAP Analytics friendly.


Multichannel access to data:

SAP Analytics Mobile solutions enable business users to access data anywhere, anytime, on the go, both online and offline. SAP Analytics Dashboards presents powerful visualizations, including charts and graphs to enable dashboard consumers to check trends on desktops as well as mobile devices.


SAP Analytics can be deployed in the cloud or on-premises or on a hybrid of both, according to the needs of the organization, thus allowing businesses to focus on their core business, instead of IT infrastructure.


The cloud solutions provided by SAP follow the data security and worldwide compliance policies, which, again, offers an advantage to organizations.


Choosing the right analytics tool is not only about being able to create reports and dashboards. If the implementation and delivery of analytics projects go smoothly, delivering results early on, the relationship between business and IT is improved.


SAP Analytics provides the kind of agility that business users are looking for: to analyze business data in real time, draw conclusions, take action, and check the response to changes made.


Introduction to SAP Analytics Tools and the Key Features of Each Tool

In a world where business requirements are changing rapidly to accommodate growth—competing with start-ups with shorter time-to-market, launching new products faster to have the first mover advantage— analytics has to keep pace to match the speed.


Every business process or product life cycle requires a feedback loop, to be able to analyze cause and effect and to improve business processes and cost-effectiveness.


This feedback loop is enabled by business analytics solutions that make valuable information about businesses available in the form of reports and dashboards.


The success of analytics projects depends on the underlying layer of data called the data warehouse, also known as the OLAP (online analytical processing) layer. The data warehouse is the repository of data fetched from source systems such as CRM, ERP, and sales and marketing systems.


Data from OLTP (online transaction processing) systems is fetched through an extraction, transformation, and load process that extracts data from source OLTP systems and cleanses, formats, and saves it in the data warehouse.


The data in the data warehouse is then presented in a business user-friendly manner in reports and dashboards. Data warehouses store both new and historical data for comparison and trend-analysis purposes, while transactional databases store only current data.


For example, a bank administers millions of transactions every day, with clients withdrawing and saving money, buying financial services, transferring funds, etc. These daily transactions are stored in operational databases, also known as OLTP. OLTP databases enable quick data retrieval when the data to be retrieved is not too huge.


However, if a bank wishes to do some trend analysis related to its most profitable customers or to determine what kind of customers make the most number of transactions over a period of time, OLTP databases are not able to handle such queries, and, therefore, a data warehouse is required.


Data warehouses are sufficiently robust, with fewer joins between tables, and they store historical data, facilitating answers to such queries as those preceding.


Data warehouses store not only historical data but also data that is blended from different source systems, both at a very granular level, such as transaction ID, as well as at an aggregated level, such as monthly or yearly turnovers.


Having data at a very granular as well as at an aggregated level makes it possible for business users to drill up and down and slice and dice data at different levels.


Having the strong foundation of a data warehouse enables business users to analyze data in depth. Building a data warehouse depends on a well-designed, scalable data model. 


SAP HANA Data Warehousing Foundation complements data warehousing capabilities provided by SAP Business Warehouse, SAP HANA Agile Data Marts, and other SAP HANA systems.


In order to integrate data from different source systems, cleanse its format, and load it into the data warehouse, SAP Data Services is used as an ETL tool. Once the data has been loaded into the data warehouse, visualization tools, such as SAP WebIntelligence, SAP Design Studio, and SAP Lumira, are used to visualize the data in an interactive manner.


The following sections describe some SAP Business Analytics solutions.


SAP BusinessObjects Business Intelligence Platform

The BusinessObjects Business Intelligence platform is a flexible platform for sharing information within the organization—from CEOs to data scientists to business users. The complexities of the underlying data are shielded by a business user-friendly layer that maps the technical data into corporate business terms.


This layer is known as the semantic layer. The semantic layer provides information in a way that is familiar to business users.


For example, employee IDs in a database might be saved either as emp_id or empl_id, which is not very clear to a nontechnical user. But renaming emp_id Employee ID in a semantic layer provides business users with the relevant data from a business perspective.


Key features of SAP BusinessObjects Business Intelligence platform are as follows:


Self-service access to information:

BI developers can publish reports into folders that are directly accessible by business users. Business users have the possibility of creating their own reports, by connecting to the semantic layer, thereby reducing IT dependency and increasing the throughput.


Data integration from different sources:

These can be merged into one holistic view to be presented to the end user. Data sources such as ERP and CRM and order-booking systems that have completely different data types and formats can be visualized in a BI platform as the semantic layer handles the complexities.


Publishing and scheduling functionality:

These make large-scale distribution possible: BI reports can be shared in different formats, such as Excel or PDF, or can be scheduled, so that users receive them in their mailboxes, or published on portals, for general consumption.



Several departments and users in one organization can take advantage of a single platform, making the SAP BusinessObjects BI platform scalable. When the number of users or the number of business areas that require the visualization functionalities of the platform increase or the number of user licenses or added data sources have to be taken into account, the BI platform remains one.


SAP BusinessObjects Dashboards

SAP BusinessObjects Dashboards provides highly interactive and visually rich dashboards. SAP Dashboards provide advanced visualization features such as maps to view geographically distributed data. Color palettes further enhance the aesthetic sense, as do integration options to embed the dashboards into portals.


SAP BusinessObjects Dashboards provides rich visualizations in the form of graphs, bullet charts, pie charts, and scorecards, with such additional features as legends and labels on graphs.


SAP BusinessObjects Dashboards has a thin processing layer between the data sources and the dashboard front end, making data refresh very quick. This is one of the major requirements of businesses: to be able to see business data in real time, in charts and graphs. Easy integration with different data sources and semantic layers highlight the ease that SAP Dashboards provides.


SAP BusinessObjects Design Studio

SAP Business Objects Design Studio enables BI developers to create analysis applications and dashboards based on SAP BW, SAP HANA, and semantic data sources, both for desktop and mobile devices.


End users can create analysis applications using SAP BusinessObjects Design Studio, eliminating the need for BI developers for every new implementation requirement.


Design Studio provides drag-and-drop features for easy navigation. With a real-time package available, Design Studio makes it possible to pull real-time data from SAP BW and SAP HANA, to create visualizations in real time.


SAP BusinessObjects Lumira

SAP BusinessObjects Lumira enables users to access, transform, and visualize data in a self-service manner for ad-hoc reporting. The easy interface with drag-and-drop features makes Lumira very user-friendly, even for the least technical business user, eliminating the need for extensive training.


It is truly a low-cost, end user–friendly tool, with easy sharing options, such as publishing Lumira datasets and documents to Lumira cloud, Lumira server, or the SAP BI server. SAP Lumira is mobile-compatible, enabling business users to access data in real time.


SAP Crystal Reports

SAP Crystal Reports provides businesses with pixel-perfect reports for external consumer needs, such as invoices, bank statements, or portfolio letters.


SAP Crystal Reports provides smart default formatting options. It also provides multi-source support and can combine data from disparate sources and present it as one holistic data set.


SAP BusinessObjects Analysis

SAP BusinessObjects Analysis comes in two versions: an edition that integrates Word and Excel for Microsoft Office, and one that is accessed from the BusinessObjects Enterprise BI launchpad in a web browser, which enables users to access data easily, using workbooks.


SAP Predictive Analytics

SAP Predictive Analytics is a statistical analysis and data-mining solution that takes advantage of predictive models to discover hidden insights and trends in data, in order to make certain predictions.


SAP Predictive Analytics combines SAP InfiniteInsight (as Automated Analytics) and SAP Predictive Analysis (as Expert Analytics) to do time series forecasting, outlier detection, trend analysis, classification analysis, segmentation analysis, and affinity analysis.


SAP Predictive Analytics can handle large volumes of data and perform data analysis by using the R statistical analysis language and in-memory data-mining capabilities.



Every business has unique requirements, which keep changing with time and growth. To support business needs, business analytics implementations must always keep abreast of such changes.


Therefore, it is important to create a scalable data warehouse, with an ETL design that can easily integrate newer source systems and handle the loading of additional amounts of data. The visualization tools chosen should be unique to each business requirement.


Some business processes require that analysts analyze the data themselves. In such cases, SAP Business Analysis represents a suitable choice. In other cases, for example, top management may have to check weekly market trends without delving into detail, and that need can be addressed by using SAP Dashboard.


Each business must carefully consider its specific requirements before selecting a business analytics tool that fits the bill.


Consolidating Data from Disparate Systems for an Analytics Project

Analytics discovers patterns in data, by turning raw data into meaningful information. Analytics implies being able to generate insights by analyzing data generated by business systems.


Because every business has several systems that support each business process, it is important that data from these different systems is merged before data analysis begins.


This is critical to attaining a holistic view of business processes and the data generated by them. This blog explains the importance of merging data and its attendant challenges and solutions.


Importance of Merging Data from Different IT Systems

Every organization, irrespective of industry, has several business processes, each supported by several IT products and processes. Each of these IT processes and products yields an insurmountable amount of information, as well as insights, which are of paramount importance for any organization.


Businesses have to rely on data to make decisions regarding investments, improving customer relationships, streamlining IT processes, optimizing human resources, etc. Before the invention of computers and state-of-the-art


IT processes and products, organizations relied on paper records in order to improve planning. Because data is so important to organizations, a great deal of time and effort is invested in maintaining the quality, relevance, and availability of data.


Organizations typically have transaction processing systems, CRM systems, billing systems, IT networking systems, human resources supporting systems, inventory management systems, and other IT systems that are industry-specific. For example, the healthcare industry uses systems that monitor heart or pulse rates, and that data is invaluable for research purposes.


With the growing needs of an organization, the number of IT systems supporting business processes also increase, each system providing support for specialized business needs.


Ever since the Internet came into being, the amount of data has been on the rise, and now, with smartphones and digitalization on the rise, businesses are inundated with data. Often, the data residing in one IT system is different from the other, making integration between systems an uphill task.


A few IT solutions that can be mapped to business processes in any organization, and the integrations required for these solutions to be able to function, are discussed in the following sections of this blog.


Order Booking Systems

Booking systems are used for creating, managing, and processing orders of any kind, be they related to retail, healthcare, manufacturing, or telecom. Order booking systems serve as the initial interface between a business and a customer.


There are different ways to track business leads and potential markets by using web analytics, but order booking is seen as a confirmation that a lead has resulted in a customer.


Order booking systems have provisions to integrate with other systems, such as ERP, inventory control, billing, invoicing, and CRM. It is crucial for a business to know the reason behind the conversion of a lead into a customer.


It could be that certain marketing campaigns lead a potential customer to book an order, such as recommending different products on a website that have caught a customer’s eye.


While booking an order, front-office representatives are required to check that the product pertaining to the order is available in the inventory, requiring integration with the inventory management system.


Once the order is booked, the product has to be shipped, which, again, requires integration with the logistics software solutions. The order booked for a product has to be charged; hence, integration with an invoicing system is crucial.


CRM Systems

These are responsible for an organization’s engagement with existing and potential customers, for sales, marketing, and customer service. One of the main success factors that leverages a high ROI on CRM initiatives is to have robust customer master data. CRM relies heavily on customer master data to mail, message, or send out notifications about campaigns.


It is thus vital not only to have the demographics of customers updated but also their preferences and interests. Sending out campaigns to customers who are not interested in certain products, or sending out multiple notifications to the wrong customers, can backfire.


In order to distribute relevant marketing campaigns for lead generation, the CRM systems not only require integration with the master data but also integration with ERP, to be aware of the product that the customer owns at present, with billing systems to check the customer’s invoicing history.


It could be a simple business requirement to remind customers about a credit card expiry date. But such a simple requirement gives rise to extracting data from multiple systems, including customer master data, billing, invoicing, and e-mail software systems.


Billing Systems

Billing systems are responsible for collecting consumption data, taking pricing into account, and calculating charges for customers. They are also responsible for processing payments and managing debt collection.


In order to calculate an invoice for a customer, a billing system has to check the product or package that the consumer owns currently, the means by which is usually an ERP system or an order booking system.


Every organization has a pricing strategy, which could very well be embedded into the billing system or stored in some other software solution.


Billing solutions have to delve into the ERP, and the pricing solutions must be able to calculate and invoice the customer. Billing systems are not only used for invoicing but also as a resource for accounting systems involving bookkeeping, budgeting, and forecasting.


Several organizations are required to run a credit history check or establish a credit ranking before granting a customer access to their products.


This, of course, requires additional integration with external sources for credit evaluation of individuals or businesses that have expressed an interest in becoming potential customers.


Campaign Management Systems

As software solutions to manage marketing campaigns, campaign management systems usually read data from CRM systems, to distribute marketing content to targeted customers. Campaign management software solutions also provide reporting or dashboarding functionalities to measure marketing campaign effectiveness.


A campaign management process involves planning, designing, testing, implementing, and execution and analysis of results, which are then used for optimizing future campaigns.


But in order to target the right customers with the right campaigns, integration between customer master data, CRM systems, invoicing systems, and web analytics are almost unavoidable.


The most effective campaigns are those that seem tailored to the customer. In order to send out personalized campaigns, businesses are required to do deep-dive into customer information from various systems, to gather as much data about the customer as possible.


E-mail software solutions that send out newsletters and other campaigns to customers could backfire and result in customers de-registering from the offered services, if not correctly configured with inputs from IT solutions that support customer lifecycle.


Web Analytics

Web analytics encompasses all tools and software solutions used to measure web traffic and online customer behavior, to optimize web usage. Web analytics is used for measuring marketing, sales, UX (user experience), landing page, bounce rates, and market research. It is also used to measure the amount of traffic that originates from social media sites.


Again, web analytics products have to be integrated with solutions such as master data or data security solutions to understand the customers that log on to web pages—their usage patterns, fraud detection, and access rights.


Web analytics is also used to measure lead generation, and the data resulting from such measurements are forwarded to CRM and campaign management systems, in order to target them better, thus increasing conversion rates for businesses. Capturing clickstream data is a huge challenge for businesses, as a web page could potentially generate millions of clicks in minutes, depending on the content of the page.


Furthermore, if there is a business requirement to combine clickstream data with other business data, to generate insights, the solution could encompass several IT systems, making it too complex.


But, nevertheless, if it is a must for revenue-generation, data integration between systems has to be achieved in the most optimal way.


Inventory Management

Inventory management systems are one of the most important business systems because they reflect business investment and the costs incurred before a product is sold.


Front-office representatives or salespeople in retail outlets have to rely on the stock available to be able to serve customers better and before making commitments with regard to the availability of any product.


Inventory management systems, in turn, have to be integrated with bookkeeping systems, logistics, ERP, and billing. A mismanaged inventory management system can be hazardous to any business; thus, it requires the utmost care and surveillance.


Human Resource Management

Human resource management (HRM) systems administer attendance, payroll, performance appraisals, benefits administration, recruiting, and learning and absence management, to name only a few functions related to personnel. 


HRM systems are a way to keep track of optimal resource utilization in any organization. Payroll, absence, and attendance are interdependent, and it eases problems if these modules have tight software integration.


Payroll and attendance are also used for budgeting and forecasting, requiring an additional integration with those systems. Many organizations not only have built payroll, attendance, absence, performance appraisals, etc., into their HRMs but also recruitment, hiring, employee self-service, and learning management, thus making them quite robust.


Master Data Management

Master data management, as the name suggests, is the administration of the principal source of the most accurate data regarding an enterprise. With innumerable IT systems and tools, data governance is of utmost importance to maintain correct information.


In fact, master data management should be considered paramount in any business. Master data management is used to remove duplicates, redundancies, and errors in data.


Customer information entered in different systems could have different names or addresses, making it difficult to combine the data from multiple sources. Master data is maintained for products, product codes, and hierarchies, partners, suppliers, locations, customer details, transactions, organization hierarchies, etc.


Every system, such as billing, ERP, or CRM, should extract the information regarding the preceding from the master data, to retrieve correct and up-to-date information.


Not only is the data correct and up-to-date, but if all systems use the master data as a single source of information, then it makes data integration much easier while reducing redundancy.


However, maintaining master data management systems involves a great deal of effort to cleanse, govern, and manage the quality and lifecycle of master data.


Mobile Apps

With the rise in the use of smartphones, most companies have invested in building mobile apps that serve customers better while on the go, be they retail companies that send promotional codes.


And the latest news about their products, banks that allow transactions on mobile apps, travel companies that store all of their customers’ travel-related documents in one app for easy accessibility, or media outlets that disseminate the latest news via mobile apps.


The app has to retrieve, from transactional systems, data about customer purchase history or customer preferences. The information on the apps about customer interaction is again fed back to response systems, to improve the app features, to come up with better pricing strategies, and to improve and streamline business processes.


Moreover, real-time data becomes important for mobile solutions, as customers demand, for example, only the latest news or the latest status related to the delivery of their package.


This real-time demand for information requires analyzing huge amounts of data that is retrieved from business systems at the same time as analysis is being undertaken.


Real-time analysis of data demands that IT systems support data blending in real time, with very little latency. Systems have to handle a variety of data, its volume, and the velocity with which it is delivered.


Business Analytics

Last but not least is business analytics solutions. These incorporate all of the systems and solutions previously discussed and must be able to measure and optimize revenue generation, customer acquisition, and business gain from a holistic point of view.


Business analytics can be used as the foundation for decision making, either by humans (e.g., corporate management) or by automated systems (e.g., campaign management systems) that distribute campaign content via e-mail to customers, based on their interests, the data for which having been fed into business analytics from web analytics or CRM.


Business analytics is a fact-based science, driving business decisions by gathering, cleansing, and analyzing data from multiple source systems, such as those mentioned previously.


It is business analytics systems that gather and store historical data from each of these business support systems, turning raw business data into valuable information that helps businesses support decision making.


For example, in order to examine a customer lifecycle, increase customer satisfaction, and win customer loyalty, organizations have to check related data, from the time the customer bought a product or service, his or her invoicing history, past purchase history, membership points, etc. This data resides in multiple IT solutions.


Adding to the list of traditional IT systems, there are now also mobile apps and web analytics data that reveals information about customer online behavior, how they browse products on websites and on mobile apps, what leads to a purchase, and what makes customers abandon shopping carts.


So, the variety of data that has to be stored and analyzed is increasing, and so is the amount of both structured data from traditional IT systems and unstructured data from social media platforms.


If data from these multiple systems are not integrated, the business value it generates is minimal. Data in silos generates very narrow visions of the business. Data duplication, redundancy, and mismatch are some of the results of data residing in silos—a complete nightmare for enterprise organizations.


The data in each of these systems contains in-depth information pertaining to the system in question. A billing system can also act as a reporting system to provide information about important billing KPIs, such as the total number of invoices sent, the total number of payments received on time or delayed.


Similarly, an ERP system can also provide some basic reporting capabilities, such as the products owned by a certain customer, or which are the most popular products bought by customers.


These isolated pockets of information do not offer sufficient insight into the business as a whole. The data residing in each of these silos is invaluable, no doubt, but it does not provide a 360-degree view of a customer or the entire customer lifecycle.


Integrating disparate IT systems to gain business insight is, therefore, imperative. It is not an option but a must. A simple query raised by the business, such as finding the product owned and the amount charged to the customer, requires the amalgamation of more than two IT solutions, making it cumbersome if they are not already integrated.


To be able to function optimally, every IT solution requires integration with several other IT solutions. Following is an example of the integration required by an ERP system, in order to obtain the most from all of its modules for asset management.


ERP requires integration with resourcing, to check the inventory for the availability of a product, integration with logistics, for shipping a product, integration with CRM, to ensure delivery and track the customer engagement with a product, and integration with financial management, to track the invoicing of a product to the customer.


Challenges Faced During Data Integration

A typical data integration scenario, is a convergence of multiple sources, including CRM, billing, campaign management, social media, and inventory management, into one single source for analytics.


It may appear simple on the surface, but it is quite daunting to integrate different sources of data with different data structures and business rules governing each solution.


However, data integration leads to insights for increasing cross-selling and up-selling, by blending data at various levels and from different divisions of a company.


Some of the main challenges posed by data integration across IT systems and solutions and across data silos follow.


Understanding Business Needs

Taking into account the business needs that are the basis for data integration is key. Any organization that focused on a data-integration roadmap must map the business requirements to the business processes and integrate the tools and solutions that support the same. Data integration is a technical challenge, involving time and resources.


It is necessary, therefore, to understand the business gain that can be achieved by integrating data from disparate systems, efficiently utilizing time and resources.


Some data integration projects launch even before a business gain has been clearly identified. This leads to the project going haywire and to time and resources not being utilized efficiently. Once business goals are defined, the processes and data required to ensure they are not very tricky to put in place.


The foundation for data integration should be nothing other than a business gain. If the organization is clear about the goals that it intends to achieve by data integration, the process of integrating two or more systems to acquire the added advantage of data blending becomes technically do-able.


Understanding Data-Quality Issues

In a data-integration project, because the sources of data are different and come from different IT solutions, the format and structure of data are not the same. Differing data types, formats, and structures present a big challenge when integrating data from disparate sources.


Moreover, some organizations have operations spread out across the globe, introducing the challenge of multilingual text characters. Some software solutions do not support certain languages.


If we consider the example of integrating customer data from a CRM system and a billing system, it is very much possible that some of the data reside in both the systems, giving rise to data duplication.


It is then of utmost importance to find out which of the data sets is the correct one and refer it to the master data and map the differing data records into the same.


By focusing on one entity, such as the customer number or the product code, the attributes that lend completeness to the entity in question get easier to understand and easier to map from different systems. For example, the customer ID is present in most IT solutions that save customer information as a single unique identity.


A customer could have product IDs in ERP systems that point to the products owned by the customer; the customer ID in billing could point to the invoicing address credit card number, and the method of payment that the customer has chosen; and the customer ID in web analytics point to the online preferences and interests the customer has.


When gathering data regarding the customer from ERP, billing, and web analytics, there should be one data record that displays the customer’s customer ID, billing ID, asset ID, and all the attributes associated with the same—billing address, online preferences, products owned, invoicing method, etc.


If there are cases in which the data contains null values or wrong data, the business has to take ownership of the data that flows into business analytics.


Business analytics solutions do not create new data; they cleanse, transform, and modify data to be presented in a way that makes it easy to draw conclusions about customer behavior, product popularity, sales, and marketing.


Data Governance Issues

Data governance is the practice of acquiring, cleaning, and distributing data, accessing rights management, and maintaining the security of data. A typical enterprise organization houses numerous departments, each department, in turn, being run by several software solutions to support business processes.


In order to avoid data duplication and redundancy while also maintaining data security, data governance comes into the picture. It is very important to have a master data management system in place that defines the key attributes of the core business.


For example, if an insurance company has 200 products with 50 subproducts under each one of them, this should be clearly defined in a master data store. Every time that a product or a subproduct is referred to or has to be validated, it is this master data source that must be used as the single source for validation or data reconciliation.


If this practice is maintained enterprise-wise, not only does it reduce the overhead of maintaining several redundant data stores, but it also eases the distribution and security issues of data.


Data governance also handles access rights to the data. Sales and marketing data, even within the same organization, can be sensitive information, and not everyone should have access to it. Data governance rules clearly define the access levels and rights that different groups or individuals are assigned within an organization.


Maintaining Historical Records

Transaction-based systems usually store snapshots of data, lacking historical records. In business analytics, historical data is of utmost importance to check past trends, compare them with prevailing trends, or predict future trends.


Transaction processing systems such as order booking have to process huge amounts of data on the fly, the emphasis being on very fast query processing and maintaining data integrity.


But if the business requirements are to be able to analyze historical data along with fresh real-time data, both have to be integrated, blended, and stored in data warehouses.


To save data from multiple sources and spanning different time periods is challenging and requires maintaining data with business rules that were applicable in the past along with data maintained according to updated business rules and, moreover, with differences in structure and format.


For example, combining historical data from a billing system with tariff plans and pricing strategies that were applicable in the past with data from an ERP system with an updated product structure is not only complicated to integrate but will also require a lot of data cleansing and processing, in order for useful insights to be generated.


Storing historical records implies great amounts of data, and that can pose a problem. It becomes necessary, then, to choose the kind of scalable hardware that is able to support growing amounts of data.


Different Data Formats in Different Systems

As discussed, data from different sources generate data in different formats, structures, and size. A customer ID in a billing system could be a character field with a length 100, while the same customer ID could be a numerical field in CRM, thus making integration between the two systems problematic.


An integral component of data integration in business analytics is the ETL part (extraction, transformation, and loading). This is the practice of extracting data from source systems, cleaning and processing it in a way that makes it possible to combine data from single or multiple, identical or disparate systems.


In the preceding example of customer ID from two different systems that have different data structures, it is in the ETL process that one of the data types is converted to match the other, to maintain conformity and make data integration possible.


Data Integration Techniques

To extract data from multiple systems, a data-integration mechanism, such as plug-ins, adapters, and APIs, has to be available. Today, most software solutions are equipped with some form of data connector to integrate with myriad data sources.


Data sources being spread over organizations can be on-premise, cloud-based, or remotely situated and have completely different interfaces with which to integrate with multiple systems.


The problem arises with the increasing complexity to connect to each of these connectors. A web service may very well have APIs available but require much tweaking and coding to be able to integrate with another source, demanding additional time and resources. Every day, more and more software solutions are offering ingenious ways of offering multisource connectivity.


Solutions to Combine Different Data Sources

Combining data from multiple heterogeneous systems is an old practice. Comparing Excel sheets stating the cost of marketing campaigns against actual sales has been a longtime standard practice.


But with new age digitalization taking the world by storm, and all business systems being supported by software solutions, combining the data from each of the sources is a critical need of contemporary businesses.


Combining data from multiple business processes yields an enterprise-wide view of data. Integrating financial data from sales, marketing, supply chains, and human resources empowers many departments in an organization to plan, implement, invest, and optimize their operations much more efficiently, with fact-based figures.


However, integrating multisource heterogeneous data entails a systematic chain of processes to refine data to an extent that it generates information that can be rendered useful by organizations. The following sections elaborate the steps used to process data during a data integration process.


Data Cleansing

Raw data from source systems is as per the standards and rules applicable to the source system, which may not necessarily be the same across all the other systems that have to be integrated. Common fields that have to be handled during this stage are date fields. Dates could be defined in different formats, depending on the source system.


Some enterprise organizations have businesses spread across the globe, each country having specific time zones and date formats. Standard business rules that are relevant to all the systems involved must be defined before applying cleaning mechanisms.


For example, an organization may choose to concatenate first names and last names of customers residing in any source system, while combining the sources or adding the country code to all phone numbers.


Removing Duplicates

It is evident that while trying to combine data from multiple sources there is bound to be duplication. Again, predefined business rules determine the method used to eliminate duplicate data records.


Let’s consider a customer in a CRM system who has purchased multiple products at different time periods. If the business requirement is to store every record in order to analyze all the products that a customer has purchased during a certain period of time, the multiple records can be considered relevant.


However, if the business requirement is to save the record pointing to the latest product acquired by the customer, then all other multiple records are considered as duplicate and must be eliminated.


Similarly, if a customer has changed address several times, is it relevant for the business to store all of the customer’s previous or only the latest?


There could also be scenarios under which the records are identical, such as a data record pertaining to customer details that may have the same information as a data record from web analytics. In such a case, the business would have to determine which record to discard.


Applying Integrated Business Rules

Consider a logistics company that calculates the estimated time of delivery of packages and compares that to the actual time in which packages are delivered.


It is the business side that determines how the estimated and actual time required for delivery of packages is derived, which touch points are to be taken into account, and which processes and rules define a delay, etc.


If the business requirement is to integrate all the source systems that contain information regarding geographic locations that the company delivers to, postal tracking systems, scanned data from handheld devices, data quality issues arise, owing to data-redundancy and data-structure differences. Business rules are, therefore, critical to maintaining the quality and relevance of data.


Clearly defined business rules, when mapped to software solutions and systems, are easy to maintain and have faster implementation cycles.


Maintaining Master Data Management Systems

The importance of master data management systems cannot be overemphasized. With businesses becoming increasingly diversified, offering innumerable services at the same time and targeting customers of various segments, the increases in complexity become manifold.


To mitigate this growing complexity, it is paramount to maintain master data records of all products on offer and all geographic locations in which customers can avail themselves of those products.


In other words, maintaining a single hub, with updated and relevant versions of the key attributes of the business, simplifies business execution and presents a single unified view of enterprise information.


Maintaining master data management aids in synchronizing financial and operational strategy and eases distribution of information to a wider audience while reducing costs, by eliminating the overhead associated with maintaining several data silos.


Avoiding Data Silos

Every company is organized in divisions, departments, or groups, such as sales, technology, and finance, that specialize in certain areas. This, of course, results in implementing software solutions that aid the specialized processes required by each distinct division or department. These isolated areas of information are called silos.


Problems arise when the data silos have to be integrated, in order to deliver, across divisions and departments, business insights to facilitate up-selling or cross-selling. Each silo has its own set of business rules, making data integration with other systems problematic. It is, thus, best to avoid data silos in the first place.


It helps to take enterprise-wide steps to avoid generating data silos, by having in place an enterprise architecture team that examines new IT investments and integration architecture.


Businesses often acquire new tools to solve short-term business goals that otherwise have a long implementation cycle. These short-term solutions gradually turn into permanent data silos. Having an enterprise architecture team that centralizes the IT investment roadmap and software solutions reduces clusters of data silos.


SAP Business Analytics Tool to Combine the Different Data Sources

As a leading business analytics product vendor, SAP has a whole suite of analytics products geared specifically either to large and medium-sized organizations. As discussed, to be able to utilize analytics to the fullest, organizations must integrate data from disparate systems.


Raw data from multiple systems make much more sense when combined to leverage insights. SAP offers SAP BusinessObjects Data Services as a tool for data integration, data quality, and data profiling.


SAP BusinessObjects Data Services (BODS) facilitates extraction, integration, and transformation of raw data from multiple business sources to deliver powerful insights for business gain.


Data and systems management tools that IT departments can use to administer BODS include

  • BI platform Central Management Console (CMC)
  • Data Services Management Console
  • Data Services Server Manager
  • Data Services Repository Manager
  • License Manager


BODS provides integration support for a number of tools, platforms, and databases, including DB2, MySQL, Oracle, SQL Server, and Teradata; a Universe connection using JDBC or ODBC; and Salesforce integration via Salesforce adapter. BODS also provides integration with big data platforms such as Hadoop.


In order to utilize the data integration and its quality and profiling abilities, to maintain an optimum base for business analytics, clear rules have to be defined with regard to architecture, business rules, cleansing, and transforming, as well as version control of the code.


Some organizations choose to have most of the data transformation logic in the ETL tools, making it easy to change or upgrade databases without many dependencies. But some organizations choose to push querying and data processing to the database level and use ETL as a means of data transfer.


  • Irrespective of the approach chosen, clear guidelines have to be defined beforehand for
  • Data cleansing and transformation
  • Understanding data dependencies
  • Error handling
  • Applicable business rules
  • Version control between development, test, and production environments
  • Documentation


BODS offers a palette of different transformations that can be used while integrating and transforming data from multiple sources. The following image gives an overview of the functions available for data transformation in BODS, including data transfer, date, hierarchy flattening, etc.

There are also a number of data-quality functions available, such as Data_Cleanse, USA_Regulatory_ Address_Cleanse, Match, etc.



You have seen the importance of data integration in business analytics to provide a 360-degree view of business data. Business analytics almost always entails integrating several business systems, to turn data into insightful information that businesses can act upon. However, easy as it may sound, data blending is far from simple.


Blending data from different business processes requires cross-platform data retrieval and storage in an enterprise data warehouse that serves as a central repository for all the business analytics requirements of an enterprise.