#AI needs #Data, Data needs a #StrategyData Needs a Strategy

As all aspects of information management are evolving, new and old technologies will have to coexist for the foreseeable future. Well-managed data is no easy task, and in these mixed and emerging environments, the equation could get more complicated.

It’s a good time to re-examine the basics by starting with three simple steps:

1) Focus on (and establish) governance. Given the nature of today’s regulatory challenges, most firms start with governance to establish guidelines that safeguard from costly missteps. Additionally, an applicable governance plan brings structure and holds the enterprise accountable to build data as an asset.

2) Evaluate data architecture and technology. By defining enterprise-wide technology standards and developing tools that can be reused, enterprises can reduce costs by promoting better planning and consistency around technology changes. Standard data protocols also reinforce governance by classifying products, customers and other areas uniformly and providing rules for where it is stored and how it travels through systems.

3) Develop or extend analytics capability by leveraging existing capabilities. The mandate must include a fresh look at business objectives and a plan to make better use of analytics to achieve those goals. For example, many use analytics and a predictive analytics model to forecast sales based on product and market data to achieve ROI. Others may decide to expand their use of analytics to analyze customer spending habits and adapt product features to certain customer segments to improve retention.

 

This article originally appeared on LinkedIn.  To read the full article, click here.