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 be 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:
Measuring campaign effectiveness
Measuring online behavior through web analytics
Master data management
Data security and compliance policies
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 digital footprint data to gain insights into their business.
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
SMS (Short Message Service)
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 with each other only when the need arises for a business requirement that transcends both business and IT.
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 effective product management lifecycle.
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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 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 to 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.
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.
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.
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.
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.
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 a 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.
The following sections describe some SAP Business Analytics solutions.
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 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.
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.
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 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 in 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 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.
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.
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.
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.
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.
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.
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.
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 on the steps used to process data during a data integration process.
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.
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
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.