6 Keys to Building Your Big Data Strategy

Staying Ahead of Your Data

Many companies have now proactively started working on data management strategies rather than being reactive to data problems. Most of the questions that our customers ask us are related to how we can help them find value from the data they already possess. Simply having a lot of data is like having a lot of ingredients in the kitchen: it’s a great start, but you need a good recipe and a talented chef to cook up something worthwhile. Here are six helpful steps to achieve success through a big data strategy:

1. Identify Your Goals

   It is essential to know your objective: Is it increasing the efficiency of your current system, growing revenues, providing valuable insights for informed decision-making, or improving marketing strategy? Having clearly defined goals makes it easier to plan and onboard resources. If your goals aren’t clear, you’re simply wasting your time, resources, and money.

2. Build a Qualified Talent Pool

   To execute your big data strategy successfully, you must have a highly skilled team in place. You’ll need talented statisticians who can glean valuable insights from data, data analysts who can communicate the insights, and experienced business analysts who can effectively and efficiently lead your team and make key decisions. Clear communication and discussion between key project stakeholders and your technical team are paramount to the success of your process, as miscommunication may result in poor execution from wrong assumptions. So, get the right team in place, and the rest will follow.

3. Standardize Your Data Storage

   When dealing with big data projects, it’s common to get data generated from different sources in different formats—making it difficult to derive a single version of the truth. It is absolutely crucial to standardize data formats and ensure any data being stored in your system conforms to the standard. Consistent data entry makes it easier to mine the data, and can also help in better decision-making.

   Also, the focus on data has caused many to rethink their job roles. As a software architect, solution architect, or software engineer, how do you plan your activities around data? It is no longer a straightforward RDBMS DB storage issue. Every single bit of information is critical and can help in driving a business forward. As a result, proper planning on how to store and use data is crucial.

   

4. Cleanse and Enrich Your Data

   How clean is your data? You might be surprised. Data cleansing is the process of detection and correction or removal of corrupt or inaccurate records from a table or database. Your data could be incomplete, inaccurate, incorrect, or irrelevant. Identifying bad data and modifying, replacing, or deleting the junk is part of the process. On the other hand, data enrichment is a value-add process in which data from multiple external sources is augmented to existing data sets to enhance quality and richness. Both of these processes are very important to any data strategy and can help ensure the information is as pure and complete as possible.

5. Plan for a Scalable, Stable, and Secure Technology Stack

   When starting any big data project, the tech stack should be in line with your objectives – a clear and complete understanding of how the product will be used is necessary. Today, the scalability of your solution matters more than the actual execution itself. The technology stack should be highly available and fault-tolerant. It must be fast and support the on-time availability of data and key decisions required for the business. However, with all this in mind, it is also important not to forget about the security and safety of your data. There are news about software and systems being hacked almost every day. This reinforces the need to encrypt and store data for safety and security.

6. Be Agile, Be Flexible

   How nimble are you? Are you up for a challenge, or open-minded enough to change course if necessary? As you work with new, disruptive technologies, you will likely encounter unexpected hurdles. These might require changing your tech tools or increasing the budget to get better insights out of data. Or, there may be a change in the business requirement or project objective. If this happens, just be ready to accept the challenge, realign yourself, and work towards making the project the best it can be. In fact, being agile and flexible to rapidly experiment and implement changes is key to achieving success in any digital transformation initiative.