3 Strategies to Prepare Data for Analytics
Do not make the mistake of focusing too much on analytics without building an information management foundation to get it right.
By Priya Singh, Information Builders
Many people would agree with the claim that analytics is the most important technology for growing business in today's hyper-competitive business climate. But you may not get the same reaction if you said data quality management is equally important for the health of the business.
If you agree data is the backbone that is powering the analytics and fueling decision-making, however, it’s logical that ensuring data is accurate is critical. For robust analytics software to do its magic in pointing business stakeholders in the right direction, data quality must be present.
Often companies do not see this link and can end up focusing too much on analytics without building an information management foundation to get it right. Data in its most basic form in the backend databases and source systems is as valuable as it is in data warehouses and reporting dashboards. In fact, the foundational data determines the quality of reporting and business intelligence 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 derived from it.
Here are three strategies to ensure your data is ready to deliver analytics:
Unify Information Assets for Operational and Analytical Needs
The complexity and time sensitivity of IT environments, along with increasing project costs and risk, necessitate a data integration strategy that should not only be able to unify information assets across legacy and distributed systems but also support of 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 utilized to improve business performance and drive competitive advantage through smarter, better decision making.
Build a Real-time Data Quality Firewall
Information consistency across applications is crucial 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, thus 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 chance to inspect 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.
Synthesize Business Critical Data into Single View
Cost, risk mitigation and revenue standardizing each factor into mastering business critical data into a single view, 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 executing analytic reports with the right information to improve customer service if it has not mastered customer records. These islands of data can lead to persistent data quality issues and inhibit meeting business needs. Creating a single view of customer records leads to 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 the business’s problems at once. It's best to think big and start small by applying incremental solutions to specific business issues, thereby allowing for easier course correction and ultimately successful MDM implementation.
Analytics may be the top of the pyramid of a successful enterprise data strategy if one uses Maslow's hierarchy of needs analogy, but businesses will still need to meet the basic needs of data integration, quality and MDM for sustainable business intelligence and analytics. An extensible, 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.
Priya Singh is director of Product Marketing for Information Builders iWay data integrity and data integration solutions.