The increase in data volume, rate of formation, and diversity were talked about a lot, especially in the 2010s, with the Big Data discourse. However, we cannot say that we can benefit from Big Data sufficiently. With the development of Cloud Computing, machines with high communication speed and processing power, we can now store and make sense of much more data. In this way, we understand photographs’ speech and produce self–acting robots whose capabilities are increasing day by day. The way to benefit from data and increase resilience is to make operational decisions on time and correctly. For this, businesses need to establish a data–based management culture.
The need for effective use of data was also increasing day by day. As a result, the results this year are more encouraging than in the past, but they also look worrisome in some respects.
Artificial Intelligence (AI) is now a well–established focus for these large, sophisticated companies. As a result, there are strong feelings that big data and Artificial Intelligence (AI) projects will add value to startups, but there are also significant concerns that traditional companies will collapse.
The terminology may change over time, but an explosion of data and the necessity to understand it never changes. Considering that Machine Learning (ML) is one of the most popular techniques for dealing with large amounts of fast-moving data, it is understandable that big data and Artificial Intelligence (AI) projects have become virtually indistinguishable from each other. There is also a situation where statistical approaches to Artificial Intelligence (AI), such as Deep Learning, are becoming more and more common. Therefore, we see traditional data analytics, Big Data, and Artificial Intelligence (AI) as constantly evolving and changing concepts.
The earned values are perhaps compatible with older technologies. However, as we believe big data and Artificial Intelligence (AI) are extensions of analytical capabilities, the most common and most likely to succeed are those related to “advanced analytics and better decisions.”
More than a quarter of companies seek a combination of innovation, disruption, fast–moving market, or data monetization initiatives. However, programs to monetize data have the lowest priority and percentage of success.
Another important and controversial issue is the slow transition of these traditional companies to a data–driven culture.
Companies need more cohesive programs to drive data–related cultural change. Many startups have created data–driven cultures from the start; This is also a primary reason why traditional companies fear being demolished by them.
Companies take one approach to deal with data-driven disruption and change to create new management roles. However, it is still unclear how the different data–driven parts (Chief Information Officer, Chief Data Officer, Chief Digital Officer, Chief Analytics Officer, etc.) will relate.
NO DATA–BASED MANAGEMENT DISCIPLINE
The most crucial human–based problem is the inability of the business side and information technology units to take joint action to increase the data–based management competence. Usually, what needs to be done technologically has been evident for years. However, it is not easy to implement what has been described due to business dynamics.
Because the business side prioritizes urgent work such as opening a new store and launching a campaign, the solutions that information technology teams can bring will not be successful if the business side does not prioritize coming up with a definition and perspective on how to manage its business.
The most crucial process–based problem is the lack of a data–based management discipline, from the top to the extreme decision–maker, about which process, which indicator, and which decision to take. If there is no such discipline, the limited number of people or data scientists will not contribute enough to the business. Even if the most accessible technological business intelligence tool is used, either the results will be subjective, or the correct results found by people who are not active in decisions will not be reflected in real–life choices and actions to a large extent.
To increase data–based management competency, it will be a recipe for success to implement a system based on a semantic infrastructure that will enable quick and accurate decision–making based on calculated indicators and insights that need to be followed, rather than leaving decision–makers alone with raw data.
Building a Data–Driven Culture
Start at the Top with Data–Driven Leadership
If you want to create a data–driven culture, you will need to eliminate the HPPO approach. While your experience as a leader is valuable, you need to back it up with data, not predictions. Instead, focus on hypothesizing and A/B testing to gain insight and guide correct behavior.
Build a Data–Aware Workforce
This makes hiring a data–savvy workforce critical now; Otherwise, it will be too late.
Your HR needs to embrace a data–driven culture when reviewing every candidate for any role within the organization. For example, when hiring a marketing executive, check if their resume mentions his analytical approach to studying market competition and building the buyer’s personality.
Eliminate the Knowledge Gap for Non–Technical Users
If you want your employees to trust data to make decisions, give them free access to as many reports and dashboards as they wish. Even your organization’s non–technical employees need to be data literate, not just data analytics. If possible, train each employee to understand and visualize data quickly. Reduce the data gap and empower your employees to make better decisions.
Let Data Guide Every Decision
Find out who has the right to view and react to data. Just the analytics team? If the answer is yes, you prevent other departments from gaining big–picture insights.
The hallmark of a successful company is that data and analytical reports back every decision. Therefore, whether big or small, every decision must be driven by data to create highly centralized data–centric approaches and information architecture.
Make Data Analytics a Strategic Priority
Businesses often start taking analytics initiatives without understanding how they will affect their existing processes. This shows a lack of clarity at the top management level and disinterested employees.
Manage Your Existing Data
You have to track and collect every data in the office so far and turn it in your favor. In the extensive data repository, you need to consider helpful information to analyze your goals further.
So that you can better understand the information stored, you need to standardize all records and choose only those that help you unlock the potential value of your Big Data. Business managers often do not have a comprehensive understanding of data. However, analytics professionals can help them understand data and align it with broader goals.
Always Remember, Data Isn’t Everything
As you adopt all these practices to create a data–driven culture, remember that your ultimate goal is to achieve greater productivity in collaboration with your talented employees.
Don’t get so immersed in data analysis and reporting that you forget to value talent.
The steady increase in the importance of Big Data and the challenges is a necessity of the modern economy and society.
The key to success; is to determine what actions your company will take, assign the necessary responsibilities for data strategy and results to the appropriate people, and then make the required changes systematically and effectively.