Building a data strategy

In Nicholas Cage’s famous role as Benjamin Franklin Gates in the movie National Treasure, he showed there are hidden riches to find if you have the curiosity and guts to decode a treasure map. Although not quite as cryptic, a data strategy is the treasure map that helps a business discover valuable insights and unlock the hidden treasures of data to drive success.

A data strategy is a manifesto for how an organization plans to drive value from data. At its core, it’s a marketing document that establishes a set of principles the organization can use to guide data investments and needs to be shared with data consumers, the delivery department, management, and any other stakeholders that touch data. This treasure map should not be kept a secret!

But not all data strategies are created equal. Some drive real change and transformation, while others gather dust on a shelf or worse cause confusion and conflict.

In a recent episode of the Technology Tangents podcast “Building a Data Strategy,” Credera’s Chief Technology Officer Jason Goth and Chief Data Scientist Vincent Yates, bring on Ian Thomas, Credera UK’s chief data officer to discuss the guiding principles to building and implementing a data strategy. A summary of their podcast conversation is below.

We’re sharing how an organization should build a data strategy, the necessary components to executing your strategy, and how a chief data officer should refine their strategy over time.

How do you map a data strategy?

It is critical for the data strategy to be built around business objectives, avoid convolution, and meet the current needs of the organization. The following items are necessary for building and executing an effective data strategy:

1. A compelling story:

The phrase "data-driven decision-making" has become a buzzword, often used without a clear understanding of what it entails or consideration for the limitations and potential drawbacks of relying solely on data. The data strategy should put a new twist on an old story. Enthusiastically explain how the data strategy will transform the organization, such as through data monetization, data mining, and self-service analytics. The chief data officer (CDO) is responsible for the why behind proposed changes in the data strategy and leading stakeholders to understand the importance of the data strategy for achieving business objectives. This storyline is critical for conveying the data strategy with stakeholders.

2. Stakeholder involvement:

The reliance on data alone, without considering other factors such as context, expertise, and intuition, can lead to flawed decisions and missed opportunities. The data strategy needs to connect those who know the business with those who own the data.

Include details on the data stakeholders and communication plans for connecting those parties. The data strategy will involve people changing the way they work, so it’s essential to be cognizant of the operating model and organization design. The collaboration of business and data-minded people removes any guesswork in the process of turning data into valuable insights. Separate departments can utilize data initiatives to unify around common goals and objectives. Data is king, but without business context the insights become meaningless numbers or fabricated key progress indicators (KPI)s.

3. An understanding of the current data landscape:

By thoroughly documenting and analyzing the current state of the data landscape, organizations can gain valuable insights into their data practices, enabling them to make informed decisions and take proactive steps to address any gaps or issues that may exist. Performing a data audit, data maturity and gap analysis, and data architecture mapping are all great ways to determine existing conditions and areas of opportunity. This provides a baseline for the data environment and KPIs. We can discover new ways of using the existing data and where the organization needs to mature through analyzing the current landscape.

4. An understanding of outstanding data needs:

By understanding the data needs and the organizational design to support it, the business can more accurately invest in enhancing the data environment. Catalog data needs, develop a framework to ingest new data sources, and determine the appropriate organizational structure to support future data products. The data strategy should not include overly detailed plans, but general ways of working. How the data will be encrypted, for example, should live in a more comprehensive technical document.

Additionally, the data strategy may call for changes in the blueprint of how the business operates, with data threaded into every fabric of the organization. Harnessing the power of data is directly tied to employee responsibilities and business processes.

5. Strong management of data:

Adopting modern data management strategies will empower the business to leverage the latest technologies to gain valuable insights on their operations, make informed decisions, and gain a competitive advantage in their industry. Determine a decision-making framework for how to store, organize, and analyze data in an efficient, accurate, and secure manner. This is not inclusive of prescriptive policies; those would live in implementation documentation.

Several key elements of master data management are cloud-based storage, big data technologies (i.e., Hadoop, Spark, NoSQL, etc.), artificial intelligence and machine learning, data governance, and data visualization. Relevant tools and technologies, along with their high-level details and uses, should be included in this section.

6. Clear service delivery parameters:

By defining and meeting service delivery parameters, the CDO can ensure they are delivering high-quality services that meet the needs and expectations of their stakeholders. The definition of success, along with objectives and key results (OKRs), will guide the priorities of the data organization. Specify service personnel and delivery methods used to facilitate data strategy execution. Parameters such as data quality, availability, privacy, and security should be measured against outlined goals in the data strategy. Data governance processes can also be defined at a high level to manage the data being delivered.

The journey from strategy to execution

The story crafted in the data strategy will be received by stakeholders with a range of responses, some with open arms and others with sharp skepticism. To implement it effectively, the CDO must assume the role of cheerleader and chief of “creative data use” to achieve business outcomes.

Due to the transformative nature of a data strategy, there may be a need to update the organizational structures and operating models. This requires cross-functional teams and significant buy-in. By maintaining focus on driving business value and clear communication to stakeholders, the resistance to the disruption caused by implementing the data strategy can be mitigated.

A data strategy’s level of success is significantly tied to how familiar stakeholders are with its goals and objectives. With defined service delivery parameters, stakeholders will understand what changes to expect and how the data organization will measure success.

Refining the treasure map

An effective data strategy is a living but mildly stubborn document that has a two-to-three-year time horizon. It should not change on a mere whim; however, the more dynamic a business is, the more frequent the updates. Generally, it is appropriate to make minor version updates every year or so as the business climate changes.

Environmental factors can disrupt the organization as well, which may require additional strategy refinement. For example, when the iPhone was released, many companies had to quickly spin up apps to stay relevant and meet consumer expectations. Typically, mild disruptions occur every six months, but large-scale changes may mean the data strategy becomes stale more quickly. Bumps along the way and dead ends are inevitable but, by relying on cross-functional teams and collaboration, new and creative ways of utilizing data can emerge.

Getting started with your data strategy

Building a data strategy requires patience; it’s the start of a journey to discover rich insights. Here at Credera, we have seasoned professionals who can partner with a business to improve their data practices.

If you want to learn more or need help improving your own data strategy, explore our data insights or contact Credera at findoutmore@credera.com.

• Technology Consulting
• Data Strategy
• Data & Analytics