Mistakes to avoid when using big data

In today’s data-driven world, information is money. Businesses around the world are investing a considerable amount of resources to mine, store, and process data that can help them uncover important patterns and other trends.

Huge data clusters, also known as big data, are goldmines for businesses. But with opportunities come challenges. Businesses need to be very careful when processing big data. Any wrong step can result in costly mistakes, hitting the business hard where it hurts the most.

Whether you already use big data or are planning to do so, you need to avoid these common mistakes.

  1. Not defining your goal

Before starting to work on your big data strategy, you need to define your goals. Think about the problems that you are facing and then try to evaluate how your big data strategy can address these issues. Make sure your big data strategy is aligned with your business needs. Rather than focusing on collecting the most easily available data, create an action plan to gather data from reputable and verifiable resources.

  1. Failing to come up with a data collection strategy

Many businesses try to collect data on a range of topics. As a business owner, you need to differentiate between data that can be turned into actionable insights to improve your decision-making process and random data sets.

To avoid confusion, create a big data roadmap that focuses on evaluating the type of information that can bring value to your business.

  1. Neglecting governance and security

Many businesses get so carried away with hardware, software, and other technical requirements that they overlook governance and security requirements. In today’s world, where privacy has become a major concern, you, as a business owner, cannot afford to commit this mistake.

Before starting your project, discuss data protection and online privacy laws with your managers, attorneys, IT head, and chief data officer. Choose the right governance strategies and invest in the best technologies. To improve security and reduce costs, move your data to the cloud.

  1. Relying too much on data scientists

Your team of data scientists, undoubtedly, has an important role to play in designing your data collection and analysis strategy, which is critical to the success of your big data initiatives. However, you cannot depend solely on this to solve business problems. To help your data scientists create data collection models, make inter-departmental teams, which include business stakeholders, that can help them understand the nature and extent of issues the business is facing.

At iMovo we can help implement a visual data analytics platform which will allow you to harness the power of your data. Particularly in such an industry where every second counts, a big data analytics platform can help you connect and visualise your data in a timely manner. We will also support you throughout the whole journey.  For more information contact us on [email protected].