How to Build a Data-driven Business We keep hearing phrases such as “big
data knows everything” or “data is the new oil”. But, in reality,
a considerable number of businesses fail to take advantage of data-driven decision-making.
Besides tech giants who have a 360-degree view of user behaviour, smaller companies non-tech
enterprises have been adopting practices that allow them to benefit from the growth
opportunities data offers at a much slower pace. If you work in a firm that is lagging behind,
then this video could be very interesting for you! In the next few minutes, we will describe
6 steps that would enable your business to reap the rewards of data-driven decision-making.
You may rightly ask ‘’But is it possible to apply a data-first mentality for a non-tech company that
doesn’t generate a ton of data?” The short answer is “yes”! There are many ways to collect
information and make decisions based on numerical evidence rather than personal taste or intuition.
Hope you’ll stay with us until the end of this video! If you enjoy the content,
please hit the ‘like’ button and subscribe to our channel as this will really help us reach
more people here on YouTube. Thanks! Now, back to business.
Building a data-driven strategy requires a significant amount of energy, dedication,
and focus across the entire organization. In the beginning, your company might not be able
to invest huge resources in this endeavour, but it will most definitely need a change
of culture. For this reason, we advise that the first step in the process is to commit to
data-driven decision-making. It’s best if this pledge comes from the top – a CEO or leadership
realizing how important data is for a necessary business transformation will inspire the rest of
team members as well. As a result, they will be empowered by a framework that promotes
transparency and allows for quick and confident decision-making based on objective factors.
The second step in the process is to formulate a data strategy that is in sync with your corporate
and business strategy. In this way, you’ll determine what type of questions data can
answer for the company’s specific needs, thus allowing your team to build an infrastructure
for collecting the most relevant information only. It is very important to be as efficient
with your resources as possible, especially early on. We don’t simply mean saving storage space, but
also allocating how developers or data engineers will spend their limited time. So, thread wisely!
Once you have outlined your pressing business questions and what type of information you need,
it’s time to initiate step 3 – collecting the data. Make sure you have the proper procedures
in place to perform quality assurance and verify that there are no problems with the data you will
later use for analysis. Of course, you can always add additional layers to your existing database
structure, but it will be great to have an idea what you’ll need in the near future
and work with that in mind. If your company is very small and cannot invest a fortune, you can
start with some modest data engineering efforts, generating only the most essential data at first.
In step 4, you can proceed to analyse what you have collected. Consider creating dashboards
for decision-makers to use for continuous performance monitoring performance. With
these first, your team will identify and undertake different improvement projects.
At this stage, your efforts will likely yield remarkable results because you have not done
many optimizations before. And what’s even better is that sometimes the data itself will
offers clues on what you should prioritize. For example, if you decide to collect UTM
information, you can find out where the uses who visited your website and purchased your product
came from. For an e-commerce business, this is an invaluable insight as it shows the effectiveness
of different marketing campaigns that the company runs. Identifying the weak points in the workflow
and emphasizing on the strong suits can make a sizeable difference for every business.
Hopefully, the quick wins from step 4 will win support across your organization and result in
additional investment. Once that happens, you can commence with step 5. This includes building
an infrastructure to leverage the power of A/B testing and rigorous statistical measurements.
All of these actions will direct boost performance.
And finally, step 6 requires you to form a team of data scientists and machine learning engineers
who can make the most of advanced analytics and ML techniques. With this, you’ll uncover
complex second-order relationships that represent opportunities you couldn’t see when relying solely
on traditional analysis. Moreover, you can use ML algorithms to solve business problems such as:
• sales forecasting, • anomaly detection,
• data storage optimization, • targeting,
• recommender modelling, • and so on.
Please remember that, before reaching an advanced stage of data maturity in your organization,
it is much better to focus on traditional forms of data analysis.
Becoming a truly data-driven business is a long and winding road,
yet the journey is very rewarding. We hope this video inspired you to take on this challenge!
One of the best ways to prepare your team is by relying on the 365 Data Science program.
We offer courses in Data Literacy, Data Strategy, Python, SQL, and Excel Fundamentals, as well as
Business analytics, Tableau, Power BI, Machine Learning, and many other topics. What is more,
we’re constantly upgrading the program with new content, such as the upcoming Data-Driven Growth
Course with industry-professionals Tina Huang and Davis Balaba who will teach you precisely how to
transform your business into a data-driven enterprise. Find out more about the program,
resources, and other services from 365 Data Science by following the link in the
description. We’d be glad to be part of your company’s data-fueled journey to success!