Data Strategy

Never before have so many data points been produced about consumer’s interests, needs, habits and behaviors. The digitization of almost all areas of life results in a gigantic potential to better understand and serve target audiences. This can be achieved through more effective and efficient advertising, better tailored products, or even completely new services.

Companies that do this and are leaders in the use of data, generate 22 percent more profit and 70 percent more revenue per employee than their competitors. Yet, data-driven decisions contribute to business success in only 38 percent of organizations. *

The opportunities to generate value via data for prospects and customers are open to every company. One could even go so far as to say that those who ignore this untapped potential simply risk losing competitiveness. With this is mind, a Data Strategy is a proved basis for the corporate success of every company.

 

What is a Data Strategy?

In order to leverage the real value of collected data and translate it into business impact, a strategic direction is needed. This sets the framework and objectives for the collection, use and activation of data and thus supports the business strategy.

FELD M has developed the following Data Strategy Framework, which ensures data use is considered holistically:

The Three Layers of the FELD M Data Strategy Framework

Business: Objectives & Use Cases

The starting point to create a Data Strategy is to define overarching Data Strategy Objectives. These objectives are derived from the corporate strategy and should in turn contribute to it. The basis for this is a deep understanding of the overarching corporate strategy.

 

The next important element in the first layer is the collection and evaluation of use cases. We use design thinking methods to develop use cases in a workshop, which achieve a clear business impact. The workshops are conducted with as many stakeholders from your company as possible. Our experience shows, the early involvement of various stakeholders guarantees a high buy-in and leads to a demonstrably higher use cases quality. FELD M therefore focuses on collaboratively creating ideas with the customer, as well as prioritizing based on feasibility and business impact.

Business Layer – Objectives & Use Cases

• In-depth understanding of the overarching Corporate Strategy
• Definition of the overarching objectives of the Data Strategy derived from the corporate strategy
• Collection and evaluation of use cases

 

Technology: Tools, Data and Analytics

To develop the defined use cases, the status quo in the areas of technology, data and analytics is determined and, depending on the needs and overall objectives, a gap and maturity analysis are carried out. The status quo is compared with the target picture for the implementation of the use cases.

Tools

• Evaluation of the existing technology stack
• Identification of technical gaps and redundancies
• Definition of the optimal architecture approach (best of breed vs. vendor specific solutions)
• Evaluation & selection of missing technologies- definition of the interaction of all solutions

Data

• Evaluation of existing data (1st, 2nd & 3rd party data)
• Identification of availability, accessibility and quality of existing data (sources)
• Definition of data gaps and possibilities to obtain missing data: 1st party (collect), 2nd party (partner), 3rd party (buy)

Analytics

• Evaluation of existing analytical approaches and competencies
• Definition of the analytical approach, e.g. heuristic or algorithmic, bespoke or out-of-the-box
• Definition of the strategic approach to data product development (in-house, externally, partnering)

 

People: Organization and Enablement

Data and technology alone do not make a successful data-driven company. What is also needed is a new mindset, processes and data competence. Therefore, the third layer of the Data Strategy Framework consists of the two elements organization and enablement i.e., the creation of framework conditions and the empowerment of employees to work in a data-driven way. With the decision to align the company in a data-driven way, questions arise, for example, as to where data-driven work is anchored within the company, whether a central data team should be installed or whether collaboration should be managed in a decentralized manner. Clarification of the different roles, transparency about data ownership or the responsibilities of data governance are also urgent and important topics. Last but not least, training and other activities must be provided for employees so that they can expand their data, tool and analytics competencies. A so-called “data mindset” is needed in the company and data-driven work must be fostered in the corporate culture.

Organization

• Definition of the approach for data-driven work (centralized vs. decentralized).
• Definition of roles & processes as well as their organizational location
• Definition & establishment of data governance
• Establishment of understanding and transparency about data ownership and cooperation between stakeholders

Enablement

• Evaluation of the competencies of the stakeholders
• Development of an approach to establish a data mindset and build data literacy in the company
• Development of an enablement program

 

Data Strategy – how does everything play together?

The first layer provides the foundation for a successful data strategy. Only if you know what goals you want to achieve with the data and what added value it can generate, does it make sense to set out on the path to developing a Data Strategy.

The focus of the second and third layers is entirely based on the individual status of the respective company, the business model and the current goals. A unified Data Strategy encompasses all elements. Starting with selected elements in a targeted manner can also be an appropriate strategy to quickly create an initial impact while conserving resources. This prioritized selection can be a first attempt to steer the enterprise towards data-driven work.

 

How do we go about working with you to develop a Data Strategy?

After we have been provided with an overview of your overarching business strategy, we collaboratively define the goals of the Data Strategy and work out current challenges your teams face. We identify, develop and prioritize use cases that have a clear business impact. We talk to as many stakeholders as possible, both data producers and data consumers in cross-functional workshops. Our methodology is based on Design & Data Thinking.

We then survey the current status quo of your company in regard to technology, data and analyses. We evaluate current organizational conditions and challenges with regard to processes, roles, skills and mindset. In interviews with representatives from the data consumers as well as producers, we identify the challenges in the use of data, with regard to technologies in the company and with a view to analytics in your teams. In a maturity assessment, this status quo is compared with the objectives manifested by the corporate strategy and the prioritized use cases.

Based on the results of the maturity assessment, the next step is to develop the Data Strategy and the necessary strategic measures. These measures serve an optimal set-up, which enables your company to work in a sustainably data-driven way.

Finally, we prioritize the measures and the developed use cases together with you and create a roadmap. We will be happy to support you also during the implementation phase – regarding the strategic measures as well as quick wins in the course of the project.

 

*Source: Capgemini Study (2020): The data-powered Enterprise: Why Organizations must strengthen their Data Mastery