Data Strategy by Edafe Onerhime

Why data strategy fails and how you can get it right

Quote: Only 3% of companies' data meets basic data quality standards - here's how to get data strategy right

Data is a valuable business asset, described as the most important currency in commerce , yet only 3% of companies’ data meets basic quality standards and the cost of data breaches to business is rising .

Here, I share how your business can avoid data strategy failure and how to get it right, leading to improved data to support business goals and reduce business risks.

In the 25 years I’ve worked with data, I found common patterns to why data strategies fail. Here are 3 key reasons:

1 You don’t have a business strategy

A successful data strategy helps your business meet its goals and protects data from risks. To produce a data strategy, you need a business strategy. You need to understand the ecosystem the business operates in, who it serves, what it wants to achieve and how it plans to do so.

From this, you can create a data strategy that is offensive (ensures the business has the data and buy-in it needs to compete) and defensive (reduces risks to the business and complies with data regulations).

A successful data strategy needs a clear business strategy.

2 You don’t know your blind spots

Every business has existing systems in place for collecting, storing, and using data. These range from paper files to digital products to manage everything from accounting to social media.

An effective data strategy starts with a discovery - an audit of the products, processes, practices and skills used in your business. This helps to identify blind spots around data, as well as risks and hidden opportunities.

A successful data strategy starts with an audit.

3 You think there’s a quick fix

Like a business strategy, a data strategy takes time to develop and implement. Rushing to develop and implement a data strategy without understanding the business, or bringing in a data specialist without any support can increase risks of blind spots, failed data initiatives and expensive software implementations.

Implementing a successful data strategy is a gradual, long term process that’s everyone’s job - it isn’t enough to simply bring in a data specialist or rush to complete in a few weeks.

A successful data strategy needs a long term process.

Get your business data strategy right

To develop and implement a data strategy that is both offensive and defensive, start with:

1. Goals: Use clear business goals to set the context for your data strategy and ensure it delivers what your business really cares about. In 2018, Affinity Water , the largest water-only supplier in the UK, shared their data strategy and how it aligns with business goals. Implementing a well developed data strategy reduces business risks and increases confidence in the data.

2. Buy-in: A data strategy belongs in the boardroom and is accepted throughout the business. William Hill , the world’s biggest bookmaker, found wide acceptance key to delight with their data-driven approach. With business-wide buy-in for a data strategy, it is likely to support business goals and be implemented successfully.

3. Understanding: A business is a complex system of people, processes and interactions. Understanding your business, especially blind spots and workarounds, is important when you develop and implement a data strategy. Skills like systems thinking can help your business spot the patterns and problems that could derail your data strategy.

4. Audits: Knowing who uses data, what for, and how is an important starting point in developing a successful data strategy. Audits can include a benchmark of data maturity and the culture of data in the business - the skills and confidence people have using data.

5. Patience: An effective data strategy is a gradual, long term process. The Financial Conduct Authority data strategy in 2013, for example, was followed up in 2014 with a reflection on the process.

6. Iteration: Using an iterative process like Design thinking can help your business develop a data strategy that has support and delivers by:

  • Fully understanding the problem
  • Exploring a wide range of solutions
  • Iterating through prototypes
  • Testing effectively
  • Implementing successfully

This article owes a great deal to Amy Hupe’s #10MoreBlogPosts movement and my amazing beta readers:

This project is maintained by ekoner