The earned values ​​are perhaps compatible with older technologies. As we believe big data and AI are extensions of analytical capabilities, the most common and most likely to succeed are those related to "advanced analytics and better decisions."

1. ANALYTICS ISN’T ENOUGH TO CREATE A DATA–DRIVEN CULTURE
Tuğçe ARSLAN

2. 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 devel-
opment of Cloud Computing, ma-
chines 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 wor-
risome in some respects.
Artificial Intelligence (AI) is
now a well–established focus for
these large, sophisticated compa-
nies. As a result, there are strong
feelings that big data and Artifi-
cial Intelligence (AI) projects will
add value to startups, but there
are also significant concerns that
traditional companies will col-
lapse.
The terminology may change
over time, but an explosion of
data and the necessity to under-
stand it never changes.
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3. 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 Intel-
ligence (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 com-
mon 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, disrup-
tion, 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 compa-
nies 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.
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4. 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. Usu-
ally, 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 launch-
ing a campaign, the solutions that information technology teams can bring will not be success-
ful 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 acces-
sible 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 imple-
ment 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 leav-
ing 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 predic-
tions. Instead, focus on hypothesizing and A/B testing to gain insight and guide correct behav-
ior.
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5. 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 re-
sume 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 deci-
sion. 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 disinter-
ested employees.
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6. 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, analyt-
ics 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 ulti-
mate 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 neces-
sary responsibilities for data strategy and results to the appropriate people, and then make the
required changes systematically and effectively.
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7. ANUMAK & COMPANY
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member companies, please visit https://www.anumak.com
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