How big data helps companies become data-driven

A data-driven organization’s end goal should be to reach a stage where each decision requires data analysis and its use flows a firm’s processes naturally. Whether your employees are working on big data processes or different tasks, everyone in the organization should be capable of exploiting data independently. The idea is to practice relevant data in day-to-day operations to make the right business decisions.

An enterprise becomes data-driven when it fosters the culture of using business intelligence and data to make better business decisions. A company would give authority to all departments and employees to access data while encouraging them to mine, explore, and implement these data-driven principles in regular business operations.

The author of the Economist Intelligence Unit Report, Jim Giles, says a notion is still prevailing that a data scientist or statistics graduate should be hired to know how to make the best use of data just like a computer security expert needed to support IT networks of a company.

This perception is incorrect. Most progressive companies have integrated data into their business strategy. They are tolerant toward questions- even contradictions- and that’s what helps them become data-driven. They are not afraid to step outside their comfort zones and provide access to an online big data certification course to their employees, so each and everyone knows what to do with the data that comes their way.

When you try to foster a data-driven culture, teams are more likely to consider data as it will help to develop better strategies while actively dealing with data. Embedding such a culture in one’s organization is not easy as teams are likely to face difficulties in making data part of business processes. Before we talk about how companies can become data-driven, let us discuss the challenges faced by them.

Challenges to solve to become data-driven

Privacy is important

The world of data-driven organizations becomes more transparent with the use of Big Data and leads to trust among companies, customers, and competition, thus driving conversion rates and sales. However, the risk of corrupted privacy, monopolization, and market manipulation has also been rising with Big Data. These risks emphasize the need for new rules, like General Data Protection Regulation (GDPR) Act, to prevent the occurrence of illegal activities.

Data remains in silos

Data silo is a fixed data repository with one department control over it. It is like grain in a farm silo isolated from other elements.

Most organizations believe in a centralized data system, but lack the right strategy in place to achieve this objective. As a result, data remains in silos and continues to be inaccessible to people who could make use of it. This factor proves to be a significant obstacle in an organization’s data-driven journey.

Data integrity

Data combined with “integrity” refers to a whole structure. A data model or a data type decides data values. Data characteristics, such as business relations, dates, lineage, definitions, need to be relevant for data to be complete. A database with imposed integrity should be designed and checked through error checking and validation routines. For example, numeric cells or columns should not accept alphabetic data to maintain data integrity.

As the bulk of data remains in silos, most companies cannot make insightful decisions. Therefore, organizations need to improve their governance and data management.

Having the right skills

A Gartner study reveals that organizations try to meet their data skills needs according to their requirements. Data science includes a delicate balance between art and science and is more than just number crunching. Data savvy people who have the required skills are not easy to find.

The demand for data scientists may be on the rise, but according to the Accenture Institute of high-performance, there is a severe shortage of talent worldwide. The Ph.D. talent pool is not just enough to bridge the gap. According to this report, the shortage for skilled data scientists is much higher in the USA with over 80 percent of new data scientist jobs not being filled between 2010 and 2011. This trend is quite alarming for industry experts, especially since virtually every domain needs data specialists.

Identifying the right technology

Today, a plethora of data solutions are available, for example, Amazon web services, and many companies are struggling to find the right one. Finding and implementing the best fit are the top challenges organizations face when shifting from a traditional model to a data-driven one.

You need to follow the right track to become a data-driven organization.

  1. Selection of relevant data

Data and modeling have revamped over the years. With a growing data volume, new opportunities have also been unveiled. The bigger the data; the more panoramic view of the business environment a person will get. This view enables people to see perspectives that were previously not possible, such as customer behavior patterns and lifecycle markers. For example, with the use of big data, retail outlets have been able to identify people’s life journeys based on their purchases. With this information, they have been able to market related products to customers with ease.

  1. Improved decision making in real time

Data is vital, and analytics models could lead to competitive advantage and higher performance that help managers predict results. The best way to building a model is to find a business opportunity and conclude different ways by which the model can improve decision making. Studies have revealed that this hypothesis-led modeling hastens decision making and managers can understand root models in functional data relationships more broadly.

  1. Implementation of data-based models

Are you working in an organization where the upper management does not trust big data-based models? This problem is likely to arise when an organization’s data culture and tactics to implement analytics clash with each other. Tools are highly technical and only highly trained experts can use them in modeling, but are still a good fit for the organization.

Due to this dilemma, companies planning to use these models shy away from implementing such models.

Bottom Line:

Initial implementations of Big data analytics might fail because an organization’s business processes are not in sync with decision-making norms. Models should be designed in such a way that allows managers align their action with company’s goals. The right approach is to have conversations with frontline managers to develop sync between existing tools and decision processes that result in efficient management of trade-offs.



Author – Danish Wadhwa,

Danish Wadhwa is a strategic thinker and an IT Pro. With more than six years of experience in the digital marketing industry, he is more than a results-driven individual. He is well-versed in providing high-end technical support, optimizing sales and automating tools to stimulate productivity for businesses.