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Data Strategy: Building the right thing the right way

Post it Data Strat Hero

High volumes of data have been on the radar of tech media and industry analysts for over a decade. Starting with big data, we moved to machine learning through data lakes and on to the data mesh solutions of today. However, many companies have not been able to reap the benefits of high-volume data. For these enterprises, data remains the largest source of unrealized value in the organization.

The solution is not to simply open the firehose and capture all possible data. The advantage of today’s tech environment is the ability to hone in on the data that makes the most difference to the business. As the nature of this data will vary from industry to industry and company to company, there is no one-size-fits-all data approach.

Gaining the most value from your data requires an enterprise-specific data strategy that aligns with business goals. These recommendations and insights are based on our collaborations with clients who partnered with us to put the right strategies in place.

What is a data strategy?

We define data strategy as a plan that defines the people, processes, and technology necessary to achieve business outcomes with data. A good data strategy facilitates scalable, long-term growth while delivering incremental value. It is a cyclical model that allows for continuous improvement:

  1. Data is created by business processes, decisions, and/or events
  2. Data is defined for understanding and consistency, controlled to ensure quality and trust, and structured for availability and consumption
  3. Data is analyzed and transformed into insights that inform decisions
  4. Based on insights, actions are taken and plans are designed in alignment with business goals and desired outcomes

Recommended steps to build your data strategy

We recommend assembling a multidisciplinary team that includes key stakeholders from the business along with tech and data/analytics professionals. As a group, walk through the planning phase of your strategy:

  1. Prioritize the problems or opportunities across that business that you want to address with a consistent approach to data.
  2. Spur divergent, out-of-the-box thinking with a “How Might We” session focused on an innovative view of the solution.
  3. Agree as a team on the preferred solution (this is usually focused on the smallest possible increment of value you can provide).

Focus your work together on building the right strategy in the right way. Once your strategy is built and put into play, continue to engage team members with periodic key learning sessions to adjust and expand the strategy over time.

Data as a product

As the amount of data created and stored grows exponentially, its business value lags. Captured data piles up and users cannot access what they need in the form they need it. If this situation persists for a period of time, value continues to decrease, and the path to a solution can become more costly and complex. Leadership may not want to commit the needed resources to a high-value data-driven solution, choosing instead to resource other, apparently simpler opportunities.

This situation is not unusual, and it can impair the magnitude of business growth that could be achieved when data is creating maximum value. Our recommendation to address this challenge is to approach data as a product.

We define a “data product” as:

  • Packaged data as a strategic asset enabling solutions that facilitate industry-pushing decision-making and intelligent application building.
  • A key growth asset for enterprises and stakeholders to make the most informed decisions toward achieving their goals.
  • The result of applying product thinking to existing datasets to enhance and exploit the ability to explore, security, automation, understanding, trustworthiness, and growth.
  • A result of a focus on the end-suer and desired business outcomes, leveraging a human-centered design approach.

The tools and skills for the data-as-product approach are the same as any product design effort. Again, this is a cyclical undertaking as product building is not a one-time effort. It’s an ongoing pursuit that creates repeatable, tunable models.

We have helped enterprise clients pursue this approach with excellent results. In one example, our client’s organization has a data marketplace where any employee or information worker can access data that wasn’t previously accessible. A new data product now exists that allows the entire company to gain value, and that product also makes recommendations for even more.

Building your first data product

We recommend a similar approach to craft your data strategy. Form a multidisciplinary team of members from business functions that need to be included in product design. Keep these key points in mind:

  • Always focus on use-cases first.
  • Build your infrastructure while building your use case, so it doesn’t get too far ahead or behind.
  • Pay attention to people and process components: Make sure you have the right mix of skills for the use cases you are going to pursue & Provide clear definitions of trustable data alongside training and enablement.

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A mini-case study

Managing data strategically and as a product allows organizations to unleash top- and bottom-line growth through new insights and customer offerings. This example can offer helpful insight into the high value of an enterprise data strategy.

Our client, a leader in the digital loyalty space, wanted to evolve into a data-driven organization. As the volume of data products increased, the client wanted to ensure that:

  • Maintenance wouldn't scale exponentially
  • They could prevent any data issues from causing the wrong decision or, worse, a poor customer experience.

  • They would remain compliant with privacy laws and maintain a clear line of sight into the location and methods of processing data.

We helped the client identify and align to their most valuable use-cases starting with personalized marketing. In collaboration, we quickly validated the quickest path to value and built high-adoption data products anchored in high-impact business strategies.

As a result of our work together, the client achieved the objectives noted above. In addition, the client:

  • Consistently sees a $5.5M weekly lift.

  • Has a fully extensible solution to allow building easier-to-use interfaces or plugging into adjacent software solutions.
  • Can easily scale novel data products while maintaining the fidelity of its data for strategic decision-making and customer-facing applications.

This is one example of how an effective data strategy offers new opportunities for strategic business growth.

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