Whether you’re a data giant like Google, or a small, family-run business, data can be an intimidating word. It doesn’t have to be, though. It all comes down to the way you manage it, and that’s what we’ll focus most on in this article.
Where to Begin?
Many businesses have amassed so much information that they are at a loss on what to do with it. Some businesses even assume they are too small to warrant the expense of developing an enterprise data strategy. The truth is – all organizations need a data strategy, regardless of their size.
If you’re wondering how you can benefit from all this data, the answer is a lot! For example, leveraging data analytics makes the decision-making process 5 times faster.
There are countless ways in which correctly organized data can benefit a company; but, essentially – they can be broken down into two main categories.
- First, enhance your current operations and decision-making using data.
- Secondly, incorporate data into your regular business procedures.
In reality, the goals you set for yourself will dictate the types of information you collect and how you analyze it.
What Is an Enterprise Data Strategy?
A company’s enterprise data strategy (EDS) is a road map for determining how data will be collected, structured, and processed depending on business priorities, data maturity level, company size and industry, and other factors.
How to Approach Building an Enterprise Data Strategy?
Involving the company’s key actors and decision-makers can help you design a better data strategy. Having their buy-in at this vital early stage will increase the likelihood that they will utilize the data in the future.
The strategy outlined below should help you better understand your company’s present data maturity level, the elements to consider before constructing your strategy, and the necessary measures to take.
Get Your Data Structured
Understanding your data at a detailed level should be your first priority.
Think about the following:
- How long will the data be stored?
- What sources will you use for data collection?
- What kind of information will you gather, and from what sources?
- How will the information get collected?
Whether you adopt a distributed or centralized approach to your data strategy will depend on the size of your business and the industry you’re in.
Set Clear Instructions for BI and Your Teams
Setting goals for Business Intelligence (BI) and identifying the people participating in the process are two of the most crucial aspects of developing a data strategy.
Setting goals for Business Intelligence (BI) and identifying the people participating in the process are two of the most crucial aspects of developing a data strategy.
The data analyst should be familiar with the structure of the gathered data and the business logic unique to their team. BI, on the other hand, needs to be focused on the data source and managing the platform to support the analyst rather than having to be informed about the operational area it is helping.
It takes longer and requires ongoing relearning when BI constantly modifies its method to accommodate the team’s unique business logic.
Allocate Ownership
After integrating your teams and BI, the next stage is deciding who will own what.
Typically, each component of the data has a different owner. For instance, one individual or team can be in charge of the operational data while another is in charge of the reporting data.
Additionally, you might need to assign owners at various points throughout the pipeline. The BI team might initially be the data owner before giving it to the analysts.
Institute Data Governance
Data collection and storage are governed by a system of rules and policies known as data governance to ensure accuracy and quality.
Regarding governance, there are two factors to consider: the cultural and technological components.
From a technological standpoint, what procedures can you automate such that no behavior modification is necessary? Simple: You must consider both the engineer’s (or source team’s) and analyst’s perspectives.
It might be challenging for engineering teams to imagine what data looks like when it enters the warehouse because it is not a fundamental element of their product or responsibility.
If the company is not data-driven and closely collaborates with its analysts, they might not immediately understand the practical advantages of the data. In this instance, the analysts can communicate that the data drives X choice. Hence, the decisions cannot be taken until the data satisfies the Y requirements.
Because they are more familiar with the firm and can observe the immediate impact, analysts find it simpler to perceive the advantages. They can understand that adhering to data governance guidelines reduces reliance on BI, which speeds up operations.
The only way to persuade the product and engineering teams to buy into the value of data and think about their data as it is exported is if the insights from the data are driving choices being made about the product.
Reevaluate Your Strategy Regularly
It doesn’t matter where you are on the data maturity scale; your data strategy will always need some fine-tuning.
Imagine, for instance, that you have added a new function to your product and are now gathering more personal information about your customers. More of a defensive stance may be necessary here. Your organization may need to adopt a decentralized approach if its growth rate exceeds a certain threshold.
You may want to take stock even if nothing has changed in how your business runs. Generally speaking, there are two main warning signs that it’s time to reevaluate your data strategy– there is a lack of trust in the evidence and impatience with the lengthy process.
In other words, you don’t want BI to take over completely, as that would take too much time. The analyst population should be given some leeway, but not so much that it becomes impossible to conclude from the results.
Every six months to a year is an excellent benchmark to revisit your plan. Get the feedback of upper management, IT, and your teams to see how they feel about the current state of things and where improvements might be made.
How to Handle Data That’s Out of Date
Data archiving! Data archiving is the process of detecting inactive data and transferring it from active systems to long-term storage systems.
Secure data archiving allows for the long-term storage and retention of data. It provides secure venues for the storage and retrieval of mission-critical information. Once stored in the archived data management system, the data remains accessible, and its integrity is maintained.
Data archiving is essential for businesses and organizations that regularly acquire new information while retaining existing data — and must be able to retrieve both types quickly.
Final Thoughts
As we witness machine learning and AI improve and expand, we can’t help but be impressed by the potential hidden in the mountains of data that businesses collect over time. But, unless data collection and data strategizing don’t get planned well, things can go south quickly. Approach your data strategy building cleverly, and you’ll be on the road to success.
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