Types of Data Strategies Every Company Needs

Our world today is more dependant on data than ever before. It governs everything from how frequently traffic lights change to what songs your favorite music apps play. In the business world, data helps companies create targeted marketing campaigns and refine their best practices to meet or exceed industry standards.

Indeed, every company uses data to some degree. However, the crux of it is how you collect, secure, share, and use data. That’s why we’re here to help you by sharing some data best practices that will improve data insights and security.

Build a scalable data architecture.

One thing you know about data is that it doesn’t stay the same. If it did, there wouldn’t be any reason for all the fuss over data analytics. It’s a good idea to have a scalable data architecture that can grow with your business and its needs.

Your data architecture describes how you collect, share, analyze, store, and secure data. You may have a ten-year plan for your data practices, much like the U.S. Government’s Federal Data Strategy, but even your best-laid plans are subject to reality. Much like your business plan, your data strategy and architecture need to be fluid enough to adapt to present realities. As your business grows and your volume of data along with it, you’ll need a scalable architecture that will serve you through your company’s growth.

Use NumPy Arrays to format and get insights from raw data.

When you collect data from data lakes, it’s in its raw format, creating disparity and increasing false negative and false positive rates in your data. Many data scientists rely on NumPy Arrays to learn specifics about raw data, enabling them to deduce insights from the data and making it easier to index and store. Python Array assignment makes it easier for data managers to format, catalog, store, and disseminate data as needed.

Eliminate data silos.

Data silos are one of the main hurdles to building a comprehensive data architecture. The best way to speed up data retrieval and usage is to eliminate these stovepipes by implementing a data warehouse. Implementing a data warehouse also reduces the risk of inaccurate or duplicate data.

Data warehouses create a logical environment in which data is easier to find. With the right data virtualization tools, you can format all your data so it’s uniform and easy to manipulate and share. As you can see, a data warehouse is like a grownup data lake. It doesn’t need as much parenting and is always ready for work.

Use analytics to forecast equipment failures, demand changes, and more.

Almost every industry utilizes analytics to help develop its business strategy and gain an edge over its competition. One of the most common data use cases is for the prediction of equipment failures and demand changes. Predictive analytics is often used for distributors’ and retailers’ supply chain management needs.

For instance, if your data tells you that your distributor’s deliveries are usually late on a certain day of every month, you can begin planning around it. Instead of having them deliver your shipment, you can find box trucks for rent during that time and handle your own deliveries, saving time and money. A good data strategy will help your company prepare for what’s ahead using historical and real-time data.

Your entire organization depends on the integrity and safety of your company data. That’s why it’s necessary to have a data strategy that helps you collect, manipulate, and share data securely. Creating a scalable data architecture, eliminating silos, automating data collection, implementing a robust data management strategy, and using analytics to optimize your use of data are great short-term goals for your data strategy. The next step is to implement a corporate strategy that capitalizes on all of your data operations.