Preparing Data for Analytics: Three Approaches for Business Growth

As the value placed on data analytics for business growth continues to intensify, businesses are often not placing as much importance on their data, and how it is managed, as is necessary.

What businesses need are strategies in place to ensure their data is prepared for analytics. There are three approaches to take in order to satisfy the basic needs of data integration, data quality and master data management (MDM) for sustainable business intelligence and analytics. In the first instance, companies need to build an information management foundation before focusing on business analytics.

Many people would agree analytics is the most important technology for growing businesses in today’s hyper-competitive climate. The latest survey from the BSA (The Software Alliance) shows that chief executives from companies of all sizes believe that at least 10 percent of their companies’ growth between now and 2019 will be related to data analytics.

However, you’re unlikely to see the same level of importance that is placed on analytics if you were to mention data quality management. If you agree that data is the backbone powering analytics and fuelling decision-making, isn’t it only logical that you would ensure data is accurate? For robust analytics software to perform to the highest standard to point workers in right direction on a daily basis, data quality is paramount.

Data, in its most basic form, in back-end databases and source systems is as valuable as it is in data warehouses and reporting dashboards. Foundational data determines the quality of reporting and business intelligence (BI) that can be derived from it. If the incoming data can’t be accessed in a timely way, or if the accuracy of the data itself is questionable, there will be a confidence gap in the analytics and information may even be different across different business divisions, for example.
Here are my three approaches to getting data ready for analytics:

1# Bring together information assets for both operational and analytical needs
The complexity and time sensitivity of IT environments, along with rising project costs and risk, demand a data integration strategy that should not only be able to unify information assets across legacy and distributed systems, but also support critical operational and analytical needs. Companies need to find efficient and effective ways to collect, consolidate, and present their diverse and disparate data to support critical, real-time reporting and analytical activities.

A best-in-class integration infrastructure can streamline data extraction, transformation and delivery, ensuring that all enterprise data, regardless of its source or format, can be utilised to better business performance and drive competitive advantage through smarter, improved decision-making.

2# Employ a real-time, data quality firewall
Information consistency across applications is vital for ensuring smooth execution of business processes. For example, new systems should seamlessly integrate with existing infrastructure (portal access, single sign-on and existing database security) while maintaining data integrity to deliver services efficiently and accurately. A data integration solution with built-in data quality management can ensure this kind of synchronicity and integrity, therefore reducing risk by maintaining data integrity when sharing information among internal and external sources.

With the data quality firewall embedded in integration processes, companies have the opportunity to examine information, ensuring its accuracy and validity before it is unified with other enterprise data, thus proactively preventing bad data from spreading and polluting other sources.

3# Combine business critical data to create a single view
Cost, risk mitigation and revenue standardising each factor into mastering business critical data into a single view. This is the third and most important step in building an information management platform that delivers superior analytics.

For example, a bank with customer records existing in multiple systems and applications will face challenges in carrying out analytic reports with the right information, which can help improve customer service, if it does not have mastered customer records. These islands of data can lead to constant data quality issues. Creating a single view of customer records will lead to an improved understanding of customers, which in turn fosters growth in desired business outcomes.

One common technology, master data management (MDM), carries a reputation as being complex and tedious. But it doesn’t have to be so if companies implement MDM incrementally with the right set of tools and solutions. Remember, mastering data is not about trying to solve all of the organisation’s problems at once. It’s best to think big and start small by applying cumulative solutions to specific business issues, which in doing so allows for easier course correction and ultimately, successful MDM implementation.

Analytics may be the top of the pyramid of a successful enterprise data strategy, if we use Maslow’s hierarchy of needs as an analogy, but businesses will still need to meet the basic needs of data integration, quality and MDM for sustainable business intelligence and analytics. An extensive, unified and comprehensive platform to enable the above best practices will enable businesses to take their analytics to the next level and bridge the confidence gap for better decision-making.


Type: White Paper


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