Growth in data and the advent of new analytical techniques, including machine learning, have put the spotlight firmly on the capability of organizations to exploit both internal and external sources for greater insight. Data-driven strategies abound, and businesses in diverse sectors are seeking new product and service opportunities in a digital environment. However, many of these initiatives fail to reach take-off point, as the business comes to appreciate that new methodologies and technologies on their own are no shortcut to improved outcomes.
The same disciplines of information management that have pertained to existing environments, such as the data warehouse and business intelligence platform, are just as crucial in the world of data lakes and explorative analytics, yet are often neglected. Little wonder, therefore, that the potential advantages of these strategies, which are very real, have become subject to an increasing degree of skepticism and disappointment.
To make solid forward progress, organizations need to start by learning to love and value all of their data, irrespective of size, source, and application. While we pay close attention to the discovery and management of cloud services or software licenses, it's unusual to find an organization doing likewise for its information assets. Similarly, in the architecture field, a genuine information architecture is typically an afterthought that trails far behind technical design work. That architecture should be establishing the informational framework and context for the business, in which the requirements for governance, management, and integration can be set.
It is also the basis for the other critical factor: trust. If big data and analytics projects are to support better insights, then those involved in the resulting decision-making must have trust in the data and the information management processes that are applied to it. They must also establish trust externally, with the customers, citizens, and business partners with whom they interact.
The application of machine learning and other artificial intelligence techniques, based on a diverse range of data sources and types, is an increasingly important part of many business strategies, but it requires strong information governance to establish value and trust, if these initiatives are to succeed.
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