By Gary Allemann, MD at Master Data Management

Recent research shows that around 90% of organizations rely on data to make predictive decisions, but these same organizations overwhelmingly agree that a backlog of data negatively affects their ability to deliver new analytics capabilities. advances. In fact, only 26% believe the quality and integrity of their data is high enough to support advanced analytics and AI.

Providing predictive analytics and AI capabilities requires a strategic plan. Yet, while 95% of companies have a data strategy in place, the same study reveals that only 29% of these companies have a clear, understood and actionable strategy.

The gap between business and technology needs to be bridged

A fundamental problem is that too often data strategy is a cookie-cutter exercise focused on the latest buzzwords. Or it can be too tactical – focusing on IT or the immediate technical challenges of the data analytics team without business alignment.

Actionable data strategies clearly show the connection between the data management capabilities offered and the achievement of business goals and objectives. Communicating the link between data and business results is also key to shifting your company’s culture to one that is data-driven rather than buzzword-driven.

Business goals can effectively be divided into three broad categories:

  • Strategic goals that aim to change the business – for example, entering new markets or launching a new product category. In many cases, these goals will be driven by senior management and will align with the corporate vision.
  • Mid-level managers are more likely to drive operational goals – for example, reducing the cost of customer service or increasing the effectiveness of marketing campaigns.
  • At the tactical level, managers must maintain or improve existing IT systems, address new regulatory and compliance requirements, and build IT capabilities to keep the business running smoothly.

Similarly, datasets can be effectively categorized by how well they support these three broad categories: data to minimize risk, data to provide enhanced insights, and data for business operations. .

Configure your data strategy for success

An actionable data strategy should start with an understanding of key goals and objectives at the strategic, operational, and tactical levels. One can then identify the datasets and capabilities essential to achieving each goal.

As more information is gathered, clear relationships and dependencies between goals will usually emerge, as will a clearer picture of required datasets and data capabilities. For example, marketing’s goal of improving customer service can be both driven and enabled by IT’s goal of providing a true 360-degree customer view. However, the need to protect customer data privacy and comply with PoPIA is a prerequisite for both.

There is an underlying reliance on customer data to achieve each of these goals. The strategy can prioritize customer data knowing that the needs of multiple key stakeholders will be met.

Define an actionable roadmap

Once it is clear where to focus, a gap and risk assessment can then be carried out. Our enterprise information framework is a useful tool for understanding the information management prerequisites that must be in place to support various initiatives.

For example, to provide a 360-degree customer view (master data management), we can identify gaps in our privacy and data quality capabilities that need to be addressed to be successful. Again, closing these capability gaps may be prioritized based on their importance to achieving multiple business goals, or business expectations may need to be managed based on current capabilities.

Of course, a roadmap can include investments in technology. But we also need to recognize that data management is about people and process. The plan should also consider training requirements – for example to foster mastery of basic data – and address adjustments to change unwanted behaviors.

Each step can be prioritized as follows:

  • Start within three months
  • Start within six months
  • Report to next year.
  • The assumption inherent in this approach is that management will revisit the strategy at least annually, and perhaps more frequently, to monitor progress and realign with changing business priorities. Some longer-term activities may become more urgent or be dropped altogether.

Keeping your data strategy relevant is key to paying off your data debt and turning your data from a liability to an asset.