Modern Data Integration for Better Decisions and Outcomes

The insatiable demand for data continues unabated. We want to gain deeper insights into market trends, customers, competitors and our business performance, but many companies are not making the progress they anticipated. And the promise of big data analytics remains largely out of their reach.

Why? Because most companies still don’t take a strategic approach to data integration. It’s laborious and time-consuming. It’s costly. And most cannot see the direct impact it has on driving business objectives while supporting risk management initiatives for governance, regulatory and compliance (GRC) requirements.

If anything, data integration has become more complex as the sources of data have exploded. Not only are companies collecting and retaining more data – multinationals have data in many countries that they struggle to integrate, manage and analyze. Moreover, companies are sharing more information with trading and supply chain partners than ever before.

Much of this data is beyond the structured transactional variety in conventional systems and databases. In fact, unstructured data – from spreadsheets and documents to Web pages and social shares – is growing exponentially faster. More companies are recognizing that this data represents a trove of knowledge that has largely gone untapped because they have been hidden in user and departmental data silos across the enterprise.

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In the software-driven economy, people expect unfettered access to data 24/7. And they are increasingly accessing this data with a mobile device. As the pace of business accelerates, companies are under increasing pressure to ensure that the right users have access to the right data in the right format – at the point of decision.

The “3 Vs” of big data are often referred to volume, variety and velocity. However, I believe the true 3 Vs are validity, veracity and value. That’s because all of this data is of little use if it’s not integrated. Inadequate data governance has resulted in data sprawl, with incomplete or inaccurate data sets driving flawed assumptions and multiple versions of models that undermine data-driven decision-making. After all, bad data at the speed of light is still bad data.

As much of this data becomes localized, it is more difficult to manage. Equipping users with a desktop data visualization tool and calling it self-service BI/analytics often disappoints both IT and business managers. Users get bogged down trying to integrate data from different sources to prepare for analysis rather than gaining the hoped-for insights. Studies show that despite the panoply of newer technologies, enterprises typically spend up to 80% of their time in business intelligence projects preparing the data for analysis.;

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