Many companies realize that the data they have is a strategic asset. However, in reality, many do not use this data to develop their company. If left unchecked, you might lose the opportunity to compete with your competitors.
Data strategy refers to the tools, processes, and rules that determine how to manage, analyze, and act on business data. Data strategy helps you make informed decisions based on something concrete.
This will usually help you to keep your data safe and well managed. Let's look at how to utilize data strategy to make decisions based on data analysis rather than intuition.
What is Data Strategy?
Data strategy is a basis for carrying out various processing of owned data. Data strategy is a long-term guidance plan that involves many people, various processes, and technology applied to solve problems and support business goals.
Data strategy is often viewed as a technical exercise, but a modern, comprehensive data strategy addresses more than just data. The exercise in creating a data strategy is an exercise for company leaders to pay attention to the following things.
- Employees need to be encouraged to use data
- Various processes to ensure that data is accessible and of high quality
- Technology that makes it possible to store, share and analyze data
The data strategy should outline a detailed plan to mature analytical capabilities and transition from making decisions based on hindsight to making decisions with foresight.
According to Gartner's Analytic Ascendancy Model, there are four stages to achieving the goals of a data strategy. The first is descriptive analysis "What happened?". Then it moves to the next stage, namely diagnostic analysis "Why did this happen?". The third is a prediction analysis "What will happen?", and the last is a perspective analysis "How can we make it happen?" The higher the value you want to achieve, the higher the level of difficulty in analyzing existing data.
Data Strategy Elements
There are several elements that can help companies carry out analysis and formulate technical requirements for compiling a data strategy. The following are elements that companies can use to start putting data strategy into practice:
Business Requirements
Data must meet certain requirements of a business to achieve strategic goals and generate real value. The first step to defining business requirements is to identify a champion, all stakeholders, and Small and Medium Enterprises (SMEs) in a company.
A champion or champion of data strategy is an executive leader who will rally support for investment. Other stakeholders and SMEs will represent specific departments or functions within the company.
The next step is to determine strategic goals and tie department activities to company goals, in this case the goals at various levels within the company must be aligned. These objectives are most effectively gathered through an interview process that begins at the executive level and continues to department leadership.
Through this process, we will discover what leaders are trying to measure, what they are trying to improve, the questions they want to answer, and the key performance indicators (KPIs) to answer those questions.
By starting to collect and document business requirements, it is a way to overcome obstacles. Many technical projects often experience obstacles related to knowledge of what a business is trying to achieve.
Data Sources and Collection
For internally discoverable data, records are made of all system sources and any barriers to gaining access to that data. You also need to determine whether the data has the right level of detail and is up to date.
Data updates can be set at the right frequency to answer questions effectively. If there is data that is not available, recording is also done first so that you can proceed to the next step.
Technology Infrastructure Requirements
Don't get caught up in the latest trends and technologies, focus on the business reasons for your initiatives. Building flexible, scalable data is a complex topic that has many options and approaches. Here are some important things to consider:
- Use a central data repository because operational systems cannot be relied on to support all analysis needs
- Find the technical skills and infrastructure to support an on-prem data warehouse or use of cloud-based solutions
- To fill the gap when there is no quantifiable data, consider the possibility of purchasing from third-party markets or macroeconomic data
- How to provide access to data such as creating reports or enabling self-service that allows user interaction with data
All of these considerations will be incorporated into the overall development and data management plan. As with most designs, the more future requirements and needs are taken into account, the more the solution will truly support the business.
Turning Data Into Knowledge
A data strategy should provide recommendations on how to apply analysis to gain important information and data visualization is key. Data visualization tools should make data look good and be easier to understand. Here are some factors to consider when choosing a data visualization tool:
- Visualizations: You need to be able to spot trends and outliers quickly to avoid confusion with poor displays.
- Story Telling: The dashboard section should display the context of the metrics and anticipate user investigation and diagnosis paths.
- Democratization of Data: Encourage the adoption of sharing and dissemination goals and determine common definitions and metrics across the company.
- Data Granularity: Being able to provide the right level of detail to the right audience.
Individuals and Processes
Becoming a data-driven company requires more than just technology. In this stage you look at the individuals within the company and the processes involved in creating, sharing, and organizing data.
Data strategy has the potential to introduce more data and data analysis than using new tools. Based on this, pay attention to consumers' needs to understand their strengths and where they need help.
When providing new tools without providing a different way of thinking, the end result will not change. In the process, many companies encounter unintentional barriers to leveraging their data in decision making.
These business processes may need to be re-engineered to incorporate data analysis. This can be achieved by documenting the steps in a process and certain reports are utilized for a decision.
Data Governance
Data governance is an element that makes it possible to share data at the enterprise level and can be analogous to the oil that lubricates the engine of analytical practices. A data governance program will ensure that:
- The calculations used throughout the company are determined based on input from the entire company
- The right people have access to the right data
- Data lineage (data origin and data changes since that origin) is defined.
Accomplishing data governance can only be done by people, not machines or tools. Data governance requires leadership and sometimes providing direction through difficult communication and coordination. Developing a data dictionary is a good start, all end-user measures and dimensions are defined in the data dictionary.
The Roadmap
The data strategy roadmap is the culmination of all the work done and what makes all the previous work actionable. Before going ahead and starting the design, building, training, or re-engineering of business processes, it is important to prioritize.
Each existing recommendation must determine the feasibility and business value commensurate with the desired results to improve the business situation. The plan created should prioritize the things that are easiest to implement with the fastest benefits for the business.
Strategy data is the basis for all needs, especially those related to business analysis, especially for companies that will switch to using data. The elements above can be important things to use to overcome challenges in processing data and support your business or company goals.
