The world is one big data problem. – Andrew McAfee, co-director of the MIT initiative on digital economy

The business organization of the 21st century is inundated with data – some of it is structured and the rest is unstructured. The enormous mass of data, commonly known as big data, needs to be systematically analyzed to get valuable insights that pave the way for success.

But, how can companies leverage big data rapidly, in an effective manner? What does it take to get the best ROI on the analysis of extremely large and complex data sets? They need to avoid 5 commonly made mistakes, while formulating a big data enablement strategy. Today, we will see what these mistakes are and how they can be avoided. 

Mistake 1 — NOT building an effective big data enablement framework

Most organizations launch initiatives to harness the immense potential of big data, but only a few use a robust framework to execute their big data enablement programs. The absence of a solid framework results in big data initiatives going haywire, resulting in a considerable loss of money and other organizational resources.

This problem can be avoided by implementing a 5-step process outlined below.

1. Identify the massive data sets that are relevant to your specific needs and extract the data sets
2. Clean the data sets to ensure there are no duplicates, inconsistencies, inaccuracies and other data anomalies
3. Use a tool such as Tableau to analyze various patterns in the big data and find out what is causing the changes in various variables such as prices and when
4. Segregate elements of data into two categories – include elements that are less likely to change over a period in the first category and list the elements that fluctuate in value in the second
5. Evaluate and implement the right Artificial Intelligence (AI) based method to analyze the big data. More about AI in big data enablement in a moment.

Mistake 2 – NOT utilizing AI-based big data analytics capabilities effectively

AI is the new trump card of businesses; revolutionary developments in AI-based technologies such as machine learning and Natural Language Processing (NLP) are dramatically altering the world of data analysis, providing highly actionable, timely insights. Unfortunately, many companies are not utilizing AI-enabled big data analytics capabilities effectively, and as a result, are losing a key competitive advantage.

A major factor that is preventing the companies from leveraging the power of AI-based big data analytics tools is their high cost. Companies must spend large sums of money to acquire these products and hire skilled data science professionals to use them efficiently, and this will affect their revenues in the short term. However, these high cost considerations can be easily obviated by the excellent returns delivered by the latest data analytics tools; a study by Forrester reveals companies that use advanced AI-powered analytics tools are 2.8 times more likely to enjoy double-digit year-over-year growth than others.

Mistake 3 – NOT moving all data to the cloud

Staying on with cost, many companies spend huge amounts of money on acquiring highly complex, expensive IT infrastructure to store massive quantities of business data and execute their big data analytics programs. This results in big data enablement costs going up considerably. Another problem faced by organizations is they cannot always fully utilize their IT infrastructure capacity, for various reasons, resulting in lower returns on their investment. 

This problem can be overcome very effectively by shifting the data to the cloud. Various companies such as Amazon Web Services (AWS) provide cloud-based data storage and analytics solutions at very affordable prices and charge only for the IT infrastructure capacity actually used. The cloud-based solution providers also offer high levels of security to your data and scalability to meet your dynamic big data analytics requirements.

Mistake 4 – NOT appointing a full-time Chief Data Officer (CDO)

Many organizations don’t understand big data enablement is a highly specialized activity and can be successful only when there is proper oversight over company-wide data initiatives. Businesses need to appoint an experienced data professional as the CDO, to efficiently coordinate and manage various data-related activities, to get the best results from their big data programs. While it is true no two companies have the same data needs, CDOs of most companies perform the following tasks.

• Facilitate seamless, systematic collection and storage of business data 
• Ensure the right data inputs are delivered at the right time
• Prevent unauthorised access, corruption and loss of the data
• Implement the best practices of using data
• Develop the right strategies to meet future data requirements

Mistake 5 – NOT allowing business users to participate in big data enablement initiatives

As seen earlier, big data enablement is a highly specialized activity and trained data professionals with advanced knowledge of statistics, AI and other branches of data science play a vital role in the success of big-data-related initiatives. However, it is also necessary to involve end-users of the big data actively in these initiatives to ensure the desired outcomes. Many organizations don’t realize the significance of involving business stakeholders in big data analytics activities.

Companies need to remember data specialists can create new data-analysis models and provide the right solutions to complex business problems, only when they get the right inputs from the business stakeholders. So, it is very important to ensure various functional teams work closely with data scientists to overcome real business challenges.

As you can see, companies can make the best use of big data by avoiding the mistakes listed above. At Mergen, we work with you to develop the right strategies to meet your big data enablement needs very effectively, with minimal expenditure.  Hope you enjoyed this post. How do you use big data? We’d love to know.