A data-driven culture is identified to be the third-most important trend in 2020, says a report by BARC’s BI Trend Monitor 2020.
By the end of 2020, 80 percent of the companies will initiate competency development in data literacy and start acknowledging their deficiency, predicts Gartner.

Organizations must now realize the potential a data-driven business strategy holds in the business market.

Although it offers huge benefits to companies, inculcating a data-driven culture is still considered a challenge. Despite having adequate data management, most of the information is used to a limited extent for making decisions.

This simply means, despite having data and resources, the company still does not rely on the data for decision-making. Such companies will take a much longer time to become data-driven.

A shift in the mindset of organizations can be a daunting challenge, yet if not pursued can be fatal.

Below are the given steps or ideas a company must start addressing: -

Data-driven culture drives the company’s growth

Companies having solid data-driven culture tend to possess brilliant managers who with their knowledge make decisions anchored in data. It is shown through their leadership qualities. At a retail bank, C-suite leaders work on evidence obtained from the market trial together and collectively decide on the product launch. However, at a tech firm, the manager spends 30 minutes before taking over the meeting based on supporting facts so that evidence-based actions can be taken. Such practices tend to propagate downwards since employees who are looking to be taken seriously needs to communicate with seniors or higher officials based on their terms. Thus, an example set by a few managers at the top-level can easily accelerate substantial shifts in companies.

Utilizing analytics to help both employees and customers

If the message of learning new skills can be presented to the employees in a better manner, at least a few of the employees will generate interest in acquiring big data skills. However, if this becomes an immediate goal that directly benefits them by helping revamp the work, time-saving, or fetching the information required daily then this might become a tough task. Many years back, an analytics team self-taught the fundamentals of cloud computing for them to continue with their project on large datasets without waiting on the IT team to cope up with their needs. However, this idea was proven foundational when the company remade its IT infrastructure. The team could do more than answering questions on analytics while sketching out the platform for the requirement of advanced analytics.

Fix basic-data issues at the earliest

This is one of the major issues most data science and big data analytics companies face. Despite making efforts companies yet face such difficulties. Void of proper data, data analysts can’t do proper data analysis, in such cases, it is difficult for a data-driven culture to take birth.

Now to avoid such challenges, top firms use slow programs to reorganize their data rather than grand programs. This way the company allows universal access to certain key measures at a time. For instance, a leading global bank constructed a standard data layer for its marketing department for a better analysis of the financing needs. This helped the company remain focused on significant and relevant key measures.
Specialized training to be provided when the time is right

Basic skills in coding are equally important for employees and thus should be included in the training program of every company. It is much more effective to train staff in specialized analytical tools and concepts to keep the proof of concept intact. Statistical confidence in analytical concepts has become an important vernacular for companies.

Explaining analytical choices is a mandatory routine

Most companies face challenges while trying to offer the best approach while rarely there is a single or a correct approach that needs to be followed. But as a data scientist you need to make different choices, the reason why it is always beneficial to ask teams how they made their first approach and why they choose the same approach over other approaches. Following this as a daily routine gives the team an in-depth understanding about which approach is a better fit and why they need to use it.

Proof of concept must be simple and robust

In analytics, a practical approach is considered and not promises. And this is proven only when proof of concepts has been made use into production.

Most companies come up with approaches such as hackathons – is a great way of improving the online process but definitely not an ideal way of getting into the underlying system of the company. Snuffing ideas could be a bad approach for companies.

A good approach is when an engineer’s proof of concept becomes a crucial part of the production. It is a good way to start by building something equally simple and sophisticated.