Foundation of a Successful Data Strategy
Our goal as a strategic Data Science and AI consulting company is to help organizations become data-driven. We’ve seen many organizations investing in technical projects which eventually were not successful and failed to be executed or adopted. The reason for this is the lack of a Data Strategy that must be built before the development of any data initiative.
What is Data Strategy
Data Strategy is a roadmap that defines your company path to become data-driven, it shows you how, when, and where to apply data to your business for maximum impact. Having a Data Strategy is what determines success when investing in Data Science and Artificial Intelligence.
At EAI we’ve developed FORCE methodology consisting of five crucial elements of creating a successful Data Strategy: Foundation, Observation, Resilience, Competence and Expansion. FORCE is not a step-by-step guide or an implementation plan but a timeless concept that will ensure the success of your data initiatives. Here we will focus on the Foundation of all your Data Strategy.
Foundation
Foundation is the first step and the key to your successful Data Strategy development. A strong Foundation consists of five key ideas: alignment, initiatives, technology, documentation, and data.
Alignment
Organizational alignment is crucial when you decide to transform your company. Without internal support from your employees, even the most excellent project will fail since those developments will not be adequately adopted and used by people. Before the start, your people need to understand what Data Science and AI are, how technology will help them personally and the organization as a whole.
The success of all your data projects comes down to shared, well-communicated values within the company, while lack of organizational alignment is the number one reason why most data science and AI projects fail to be executed or adopted.
Goals
When an organization is aligned, everyone is working in the same direction and aware of the same goals. You probably already have your long-term vision, i.e., business strategy built. However, it would also be helpful to turn your strategy into concrete goals and divide them into measurable objectives and actions that employees should take to achieve the overall goal. Many frameworks exist to measure goals and objectives, companies are increasingly introducing the concept of OKRs (Objective Key Results) and KPIs (Key Performance Indicators) which is also a way to monitor performance and direction.
In a data-driven company vision, the goals and state of day-to-day operations can be measured and driven with data. These measurements are essential to building a successful Data Strategy because your plans will be based on them and then measured against previously defined metrics.
Initiatives
Important note: if you want your projects to be accepted and adopted in the organization, they must be user-centered, i.e. based on people’s actual needs and demands, with feedback from the right people. The right people are rarely data scientists because these are usually not the ones facing the problems they are solving. To define the right initiatives, you need to focus on the highest priority needs and problems, learn as much from the people that face them, and use technology to solve them.
You should have at least the following items to properly describe your initiative:
Name;
Description;
Feasibility:
Necessary data;
Involved stakeholders, their awareness and availability;
Essential infrastructure to deploy the solution;
Necessary integrations with the current systems;
Associated objectives and expected impact on each of them;
Associated indicators and expected impact on each of them.
This description will help you track your initiatives and their results.
Technology
You might know exactly what you need to know, what data you need, the needs you are addressing, which people are involved and have your organization aligned but without technology, you won’t go far.
To choose the right technology you need to consider five points:
Extensibility: easy to extend with new modules;
High interoperability: with easy to understand interfaces that allow integration with other services;
Portable compatibility: always choose something that is independent of the OS;
Low maintenance: you can choose between your own platform that you need to support or get a managed service;
Open-source: open-source software allows you for flexibility and agility, speed, cost-effectiveness and solid information security.
Data and Need for Documentation
Rule of thumb: To remove dependencies and manage knowledge across your organization, you must define documentation standards. Otherwise, every time a new person joins the project, he will be completely lost and will require a lot of attention; every time someone is unavailable, it will be unclear what that person has done, and if changes are required questions will arise; and every time someone leaves the project, you will have problems. Without documentation, no one will fully understand how the project works.
Finally, it should be very clear where all this information can be found. To easily access and use information, your data must be aggregated from many systems within an organization into one place. It is called a Single Source of Truth (SSOT).
The most important benefit of SSOT is to ensure that organizations operate from the same standardized version of data across the entire company. Without a Single Source of Truth, every team, department, or subsidiary of an organization is a potential data warehouse, isolated from the rest. It also enables better communication between the various business units. Implementing a single source of truth enables decision-makers to make data-driven decisions based on data from the entire organization rather than on fragmented black boxes.
If you want to learn about the next modules of the FORCE methodology (Observation, Resilience, Competence, Expansion), read our book “Data Strategy and Why Data Science Projects Fail” on https://forcebook.ai.