Building an Enterprise Data Strategy That Lasts

Arnab Roy ChowdhuryMon, 09/23/2019 - 08:51

Data is a valuable resource. A well-thought-out data strategy helps in converting the data into a useful version of itself. It ensures that the concerned employees work in coordination. Also, it enables them to derive the most meaningful insights from the data. With a plethora of raw information, it becomes difficult to keep track of data. A data strategy helps in implementing a formal approach to arranging data.

All enterprises produce massive quantities of data every hour. In an era of fierce competition, an effective enterprise data strategy is a must for continuous innovation. There was a time when business owners relied on their gut for decision making. But gone are those days when instinct was a reliable choice for business decisions. Climbing the corporate ladder and reaching the top is every enterprise's dream.

It’s important to have a lasting data strategy for facilitating the management of data for the long term. Besides this, a data strategy enables the employees to use data as an asset for as long as the enterprise runs. Moreover, the strategy should serve the firm in the long run. So, in this post, we're going to discuss building an enterprise data strategy that lasts.

What Is Enterprise Data Strategy?

Enterprise data strategy is a vision for how an organization collects, stores, manages, shares and uses data. We know that data is capable of enabling and inspiring business strategy. It defines the way enterprises target at an organizational level.

For instance, companies set a target as to how much revenue they wish to generate within a time frame. Setting the revenue goal requires a detailed analysis of the previous years’ sales data, strategies, and pitfalls. It suggests how a company can take advantage of different data activities. Thus, an enterprise can use insights obtained from data activities to maximize revenues.

The Importance of Building Sound Enterprise Data Strategy

Adaptability is of prime importance for businesses these days. For that, it's preferred for a company to adopt an agile framework. Building an enterprise data strategy makes a firm as agile as possible. After all, businesses equipped for adjusting with changes are successful for the long term. They adapt to changes well and welcome innovations.

A firm also needs to exercise proper fund management. Every task should have a preset budget. Data strategy can aid a lot in cost-cutting. By monitoring sales data for a considerable amount of time, you can spot patterns. These sales patterns can help you gather insights to make the next move. As a result, businesses see better revenue.

Most businesses tend to collect too much data without knowing what they wish to extract. A long-lasting data strategy also helps a company stay ahead of the competition. It provides a competitive advantage by preventing an enterprise from drowning in data. By generating more revenue and helping a business create and reach strategic goals, it helps a firm stand out in the crowd.

How to Build an Enterprise Data Strategy That Lasts

Every company has goals that they strive to fulfill within the stipulated time frame. A data enterprise strategy is a push in the direction of achieving the set targets. Building on that, let's take a look at how you can build an enterprise data strategy that lasts.

Determining Enterprise Goals and Objectives

How can you achieve something if you aren't clear about what you're going after? One of the most important steps for goal setting includes the accuracy of the target. Having every aspect of the goal clear aids in drafting a flawless data strategy.

For instance, if you’ve decided your target audience, then you need to extract relevant past patterns from the data available. Deciding a target audience is a must to ensure that your product reaches the right demographics. For deciding your target audience, you need to determine their historical patterns—meaning you have to check which type of audience your product catered to in the past.

Defining past patterns can help you gain insight into previous conversion rates. This further helps in narrowing down the audience. It can be overwhelming and insightful at the same time. But if you know your goals well, sorting out the useful stuff from the clutter will be a piece of cake. Demarcating clear roles for team leaders and members is a must. It divides tasks and reduces confusion.

Finding the Right Data and Integrating It

A wide array of sources containing all sorts of data can seem like a boon. When it comes to information, the more, the better. But what if the information you have overwhelms you? What if it comes to the extent that it increases your nervousness?

Firms have to realize that data isn't a mere by-product of different business activities. You have to find the right data and extract vital knowledge to implement to reach your goal. After you find the right data, integrating it helps in keeping the business running smoothly.

An efficient way to find and integrate the right data is by using extract, transform, and load (ETL) tools. Tools like Ab Initio and Informatica help the users to extract data from an application, change it into a new format, and load the transformed data into the new application. You can also use data integration platforms or APIs.

Building an Efficient Analytics Foundation

First of all, an entire business depends on the foundation it rests on. Proper analytics are the foundation of any business, so you might want to learn more about it. Experiment with new tools and use them in your data collection efforts.

Customer data platforms (CDPs) are noteworthy tools that help in building a sturdy analytics foundation. In fact, with these, you don't need to implement multiple tools. All you need to do is send the information to a CDP. It'll automatically translate the data to all these tools. As a result, data analysis improves without spending a fortune on hiring multiple data analysts.

Increasing Data Visibility and Accessibility

After sifting through the data and picking out relevant details, store it properly. You can't put data to good use unless it's accessible to the right people. If a team member needs information, its retrieval should be quick and effortless. After converting raw data into a usable format, share its access wisely. Make it actionable by enhancing its visibility.

You can use tools like Tableau or QlikView, which automate data loading from all sorts of databases. Along with that, these tools also make reporting and analysis easier, thereby increasing data visibility. When employees understand data better, it reduces the wastage of time. They can then use their time on other tasks for the progress of the company.

Creating a Data Strategy Plan

An effective strategy calls for a step-by-step approach. The same goes for an enterprise data strategy. Identify the data type you mean to collect. Also, take note of where it's coming from. The best way to achieve something is to map out all the steps in between. Disorganized data gives rise to redundancies and vagueness. Precision is a must for exercising a data strategy efficiently.

For that, it's important to eliminate vague data elements, which may include duplicate data or data from any old record that's no longer required. After that, plan exactly how you should go about the entire process. Also, keep the steps as accurate as possible. A good data strategy plan should ideally include the following components.

Semantics

A glossary containing all the data-related topics, definitions, and usage guidelines. Goals: A model that explains the importance of the data strategy, roadmap, and the goal.

Strategic Principles

Common methodologies, best practices, and standards the organization must adhere to.

Current State

Next, the current state—documentation explaining how the data operations of the organization work currently.

Data Governance Model

A model explaining the management of usability, security, availability, and integrity of data used in the enterprise.

Data Management Guidance

Processes and standards that guide to manage data elements.

Reference Architecture

Finally, an architecture that considers the organization's existing standards and allows integration of new standards.

Overall, a hybrid model is created that supports the growth and evolution of the organization.

Eliminating Data Silos

An agile system eliminates data silos. Siloed data in different departments is no good. Since the accessibility is compromised, team members can't put it to good use. As a result, it compromises the data strategy. Moreover, even the basic functions like defining and governing data can't be done efficiently without eliminating silos!

First, acquiring data and its preparation eats up a lot of time. Also, shared IT infrastructure eliminates conflicting opinions. Remember the "sharing is caring" principle.

Along with it, increase interdepartmental transparency as much as possible. Sharing customers' sensitive information is one of the legal exceptions. You can't share it with all departments. But as long as there are no legal limitations, keep everyone informed.

Implementing the Roadmap Data Strategy Step-by-Step

Once you've created a roadmap data strategy, carefully study all the stages. Analyzing and using data is the responsibility of employees. This step involves the implementation of what we mapped out earlier. When you've thoroughly considered all the steps, begin implementing.

Having a roadmap also avoids redundancies. For instance, in the absence of a plan, employees have to process data every time a project comes up. Setting data management rules is an integral part of the roadmap. With this, employees can always refer to the data governance rules for further actions.

Implementing Detailed Data Testing

In an agile framework, the testing team performs frequent testing of an application. After each iteration in the SDLC, testers retrieve data. By incorporating it into the enterprise data strategy, the teams can fix errors efficiently. Sometimes, testers can use some procedures as standard scripts for recurring patterns. Thus, the implementation of detailed data testing can help an enterprise build a long-lasting data strategy.

Summing It Up

With a vast amount of data produced every day, there are many data-related issues. Usually, when an issue arises, most teams focus entirely on the problem at hand. They try to find solutions to that particular problem. But in the process, they lose track of the cause of the data issue. When they fail to fix the cause, other similar issues arise frequently. Enterprise data strategy helps in looking at the big picture. It enables companies to take steps beyond project boundaries. It also helps in the efficient utilization of resources. In addition to that, a data strategy greatly reduces data-related costs.

As a business grows, the need to handle massive chunks of information increases. A data enterprise strategy maintains the relevance of information. It's not merely about gathering and sorting out information. A long-lasting enterprise data strategy is a tactful blend of preserving important information and eliminating fluff. You should also keep the scope of scaling in your strategy for balancing changing needs. A firm has to be smart enough to effectively tackle data. Thus, you can use it as one of the biggest competitive advantages for your firm.