6 Key Elements to Build Your Big Data Strategy

6 Key Elements to Build Your Big Data Strategy

Good data is like gold. Every industry — healthcare, finance, agriculture, omni-channel retail, logistics, and more — runs on data and relies on it as a key factor for increasing revenues and cost savings. Data is the tool that organizations bank on for smart decision making, and is absolutely necessary in such fiercely competitive markets. In fact, big data and analytics are at the top of priority list for organizations today with major work happening around data dashboards, KPIs, reports and visualizations. Analytics and Data Science are also key dimensions to consider in any digital transformation initiative.

According to an updated Digital Universe study, it is estimated that in the next few years, the amount of digital data produced will exceed 40 zettabytes — that’s 5,200 GB of data for every man, woman and child on Earth. Now that’s a lot of data. Across the globe, as more and more eCommerce businesses are setting up shop online, billions of online transactions are incessantly producing highly valuable data 24 hours a day, and there’s no end in sight. Modern enterprises are now realizing its limitless potential and thinking of ways to unlock the true value of the data they possess.

But what exactly is data? For our purposes, data is transmittable and storable computer information. Its format can be unstructured, semi-structured or structured, or any combination of the three. And though it can exist in many forms, the most exciting things are happening with Big data.

Getting the Big Picture

Big data is comprised of highly complex information sets that are so monstrously large, traditional processing software can’t handle them. Recent technological advancements have allowed businesses to harness the power of these massive volumes of data, allowing them to find solutions to problems that were unsolvable before. And with this huge chunks of data, businesses can get meaningful insights on key success factors, areas to address, predictive analytics based on current datasets and more. Big data can also help provide a strategic roadmap for better results, improved revenue — and even new revenue streams.

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 with 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.

This new focus on data has caused many to rethink their job roles as well. As a software architect, solution architect or a software engineer, how do we plan our activities around data? It is no longer a straightforward RDBMS DB Storage. Every single bit of information is critical and can help in driving a business forward. As a result, proper planning on how to use and store data is extremely crucial. Furthermore, here are six helpful elements to achieve success with your big data strategy:

1. Identify Your Goals.

It is essential to know your objectives, whether it is increasing the efficiency of the 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

For your big data projects to be a big success, it’s extremely important to have a highly-skilled team. You need talented statisticians who can glean valuable insights from data, business analysts who can communicate the insights to experienced decision makers who can effectively and efficiently lead your team and make key decisions. Clear communication and discussion between key project stakeholders and the technical team is paramount to the success of the project, as miscommunication may result in bad execution with each side making 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 truth. So 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.

4. Data Cleansing and Enrichment

How clean is your data? You might be surprised. Data cleansing (or data cleaning) is the process of detection and correction or removal of corrupt or inaccurate records from a record set, table, or database. Your data could be incomplete, inaccurate, incorrect, or irrelevant. Identifying such 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. Scalable, Stable and Secure Technology Stack

When starting any big data project, the technology stack should be in line with its objectives – a clear and complete understanding of how the product will be used is necessary. Today, scalability of the solution matters more than the actual execution itself. The technology stack should be highly available and fault tolerant. It must be fast, and support on-time availability of data and key decisions required for the business. However, with all this in mind, it is also very crucial to never forget about the security and safety of your data. There is news about various softwares and major systems being hacked almost everyday – It just reinforces the fact that we need to understand the importance of how we encrypt and store data and take necessary steps for safety and security.

6. Agility and Flexibility

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 some unexpected hurdles. These might require changing the technology or increasing the budget to get better insights out of data. And there may even be a change in business requirement or project objective. If this happens, just be ready to accept the challenge, align 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 achieve success in any digital transformation initiative.
If you haven’t yet taken a step back and thought of how you can unlock the value of the data you already possess to generate new revenue streams for your business, give us a shout out – We have helped many other clients and we’d be happy to help you. We start with reports, visualizations and dashboards leading to advanced analytics with predictive models that provide significant business advantages.

Share Article: