Is Your Company Making Any of These 3 Data Strategy Mistakes?

You’d be hard-pressed to find a company that isn’t aiming to become data-driven these days. But the truth is, many enterprises have a long way to go before they can realize this objective and reap the benefits.

According to a 2019 executive survey from NewVantage Partners, 69 percent of businesses surveyed have not yet forged a data-driven organization. More than half of leaders admitted their company is not yet treating data as a business asset and that they’re not yet competing in terms of data and analytics performance.

The first step toward becoming truly data-driven is laying down a strategy one capable of designing and guiding all the pieces of the puzzle that need to fall into place in order for an enterprise to take full advantage of its data potential.

Also Read – Why is Data very Important for Companies?

It’s worth asking: Is your company making any of these 3 data strategy mistakes?

1. Failing to Align Business Objectives and Data – 

With the amount of information available today, it’s all too easy to lose sight of the forest for the trees, so to speak. As business and tech expert Bernard Marr notes, companies commonly experience the pitfall of creating a data strategy around interesting or easy-to-implement use cases, rather than those that tie into overall business objectives.

Simply put, data strategy — and all subsequent actions stemming from the strategy — need to align with business objectives and challenges from the get-go. Data can become the means to address pain points and improve performance in targeted areas, but only if those goals and challenges are clearly defined and baked into the strategy from day one.

2. Assuming User Adoption Will Take Care of Itself –

The business intelligence technologies and architecture around which a company implements its strategy are, of course, important. Expecting business users to depend on legacy tech complete with data siloes can stop a strategic plan in its tracks.

However, only 7.5 percent of executives cite technology as a challenge to the adoption of BI — likely in part because the advanced platforms available today promote data democratization for all users, embedded analytics and advanced AI capabilities with a click. An overwhelming 93 percent of leaders instead see “people and process issues” as the main roadblock to adoption.

It’s impossible to overstate the importance of making organizational buy-in a priority — from the C-suite all the way down to everyday users. Promoting data literacy is key to ensuring everyone is speaking the same language when it comes to data analysis and usage, and to make sure everyone feels confident using BI tools and incorporating their findings into routine decision-making processes.

Earning stakeholder buy-in and adoption goes beyond tools and procedural training alone — it also requires demonstrating the benefits of harnessing data analytics by role. A marketer used to requesting reports from the IT team might continue to think of that as the IT team’s domain, not theirs, unless you demonstrate the benefits of self-service BI to them: faster reports, interactive data visualizations, easier sharing with colleagues and recognition from leaders for diving into the data on their own.

3. Underestimating the Need for Culture Change –

Becoming data-driven requires an entire cultural shift in terms of how employees think, act and communicate.

Research firm Gartner recommends enterprises take these steps to continually build a data-driven company culture:

• Bring in the best data-driven talent to lead the data transformation, including creating new roles if necessary.
• Ensure all leaders are, in fact, leading by example in regard to their own data usage.
• Maintain data quality and transparency to ensure users are able to trust what the data is telling them.
• Include data-driven decision making in employee performance reviews and goal setting.

 At the end of the day, a successful data strategy must account for business goals, BI technology, people and processes.

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