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Top 10 Big Data Mistakes and How to Avoid Them

Today, organizations of all sizes are deploying big data solutions to derive actionable insights from their structured and unstructured data and improve their business outcomes. A study by CIO Insight found that 65% of global C-suite execs believe that their organizations will risk becoming irrelevant or uncompetitive if they do not embrace big data.

For a typical Fortune 1000 company, merely 10% increase in data accessibility can result in more than $65 million additional net income. However, in reality, the path to success for companies implementing big data solutions is not very straight forward, and organizations may face many challenges along the way.

Here we’ve discussed how organizations need to be careful and avoid some of the common pitfalls while implementing big data projects to increase their chances of success.

Major Big Data Mistakes Committed by Organizations

Focusing on wrong part of the value chain

Identifying the right business problems to solve is one of the most critical elements of successful big data projects. Rather than starting with which technology to use or how to build the data models, businesses first need to identify the exact business issues, when solved, can generate high business value.

Neglecting data preparation

The process of big data requires companies to prepare the data before processing and also provide more inputs during the subsequent processing cycles. Most companies tend to skip this vital step as a result of which they may not get the right results with big data.

Collecting random data

Organizations have access to vast amounts of both structured and unstructured data, but the ultimate challenge lies in making efficient use of this data in real-time and responding to the dynamic market conditions. Besides, most companies get overwhelmed with the sheer quantity of data and end up using less accurate and inconsistent data which may not meet their business goals. Yet another common mistake is not categorizing data in the initial stages itself, which may become cumbersome in the later stages in deriving micro insights from the data.

Giving no power in the hands of business users

Business users may or may not be tech-savvy, but that should not stop them from leveraging the benefits of big data and deriving insights from that. If your big data project has a heavy dependency on the IT teams for insights, then it is designed for failure. Business users need real-time insights and cannot wait for IT teams to share reports with them once in a while. Business users should be able to slice and dice the data on their own using self-service tools.

Relying solely on data scientists for solving business problems

Data scientists have advanced and deep knowledge of statistics, machine learning and other allied areas of data science which are critical to the success of big data initiatives within the organization. They may be able to create new models and solve complex equations, but they obviously need assistance from the business stakeholders to work on the real business issues and challenges. Only when the teams work together, big data can assist in solving the real business problems.

Focusing too much on technology instead of business requirements

One of the biggest mistakes that businesses make is to focus heavily on the technology infrastructure needs for implementing big data solutions rather than looking at the business aspects which are more critical. Their primary concerns are storage, computing capacity which may be required etc. and, therefore, they end up taking decisions which are more technology-centric rather than business-centric. The primary focus needs to be on the business outcomes that may result from big data initiatives in the organization.

Concentrating on data processing at the cost of analysis

The biggest challenge that lies in big data is to design the best algorithms, models, and approaches that may be able to process the vast amount of information that exists within the organizations. However, only processing of the data is not useful unless it is made available for analysis in the right format. Often, a majority of the companies are more worried about how to process the huge volume of data which they have and ignore data analysis completely in the process.

Ignoring the end customers

One of the most common mistakes that companies commit is to generalize the needs of their customers while designing their big data solutions and often overlook the complex character of different customers and their varying needs. It’s necessary that they take time to listen and understand their needs and design customized solutions to reap better benefits with big data.

Neglecting the importance of organization cultural shift

There are many challenges posed by the organization in the adoption of big data practices as they lack the commitment for building a shared vision that needs to be deeply ingrained as part of their corporate culture and values. Data-driven decisions need to be adopted across all levels and departments. Only when the business users realize the value of leveraging data in their decision-making, they will use the tools.

Investing money in big data too quickly

Most companies make the mistake of going overboard on spending for big data solutions in the initial stages itself. A robust and customized solution does require a sound investment, but it’s important to take a while to assess the business needs and carefully invest in technologies that can help in achieving specific business goals.

Tips for help you succeed with big data projects

Have a strategic big data roadmap

Most companies have a huge volume of data but are unable to manage it in the right manner to make profitable business decisions. Hence, the key is to chalk out a big data roadmap which can help them focus on both short-term and long-term business decisions and assess what kind of information can bring greater value to their business.

Experiment with the data and keep a watch on the market

Companies deploying big data solutions must be able to integrate small and big data to get a holistic view of consumer behavior to help them understand the causes behind specific actions taken by their customers. This can also help them better assess the trends in the market for the future and take appropriate business decisions.

Develop process for data interpretation for better results

The most critical factor for success in big data is how fast companies act on the data and develop an understanding from it. There needs to be a methodical process for interpreting data so that individuals don’t just arrive at a conclusion or use it as a basis to strengthen their existing beliefs. Companies must collect, analyze, and interpret data accurately and act on it quickly to get better success with big data solutions.

Move data to the cloud to improve security and reduce costs

Big data deployment requires considerable investment in storage space which can increase the infrastructure costs. Hence, it may be more economical to move data storage to the cloud to enhance security and have access to more advanced features of the cloud platform.


It’s important to be well-prepared before deploying big data solutions as the best of technologies may fail, if certain critical factors are not taking into account while making key business decisions.

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