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SEVEN MYTHS OF DATA ANALYTICS DEBUNKED

The rapid growth and success of the data analytics field has a dark side.

--------------------------------------------------------------------------------

It has led some in the field to fall victim to myths on what can be achieved
with data analytics. At their worst, believing these myths can set data experts
up for failure. To keep data enthusiasm high and to provide valuable & useful
insights from your data analytics initiatives, focus on debunking these 7 myths.

Myth 1: That all data is created equal in analytics

A common theme many companies have is that ‘good enough data’ is good enough and
this level of data quality is going to drive world-changing insights. The
problem with this view is that it completely ignores data quality and strategy.

In a large organization, you will have terabytes and more of data. What is worth
your time to analyze? That’s the strategy question. Next, you need to explore
the quality of the data you do have.

Myth 2: Predicting the future is easy with data analytics

Another data analytics myth you hear is that predicting future behavior or
actions is so easy with a data management strategy. But if the strategy doesn’t
include master data management in support of data analytics, then what happens
for example, when you open a new division of your company? Alternatively, you
wonder if you should double the marketing spend for your top product? According
to some gurus, data analytics can solve these types of issues by predicting the
future.

For the most part, that is just not possible especially without
a master data component.

The reality is that predicting the future is more like probability analysis and
recommendations. At their best, you have value-creating predictions like
Amazon’s recommendations engine. At their worst, predictions give you false
confidence. Our advice? Approach prediction and forecasting with caution. Make a
lot of small bets and gradually test your models and make sure you have a clear
data management strategy at the core of your analytics program.

Myth 3: You need to hire a team of data scientists

A few years ago, building a data analytics function required top of the market
data talent. That’s still needed if your organization wants to produce
cutting-edge data products and software.

For the rest of us? You can go a long way through a combination of three
factors.
First, set realistic data analytics expectations based on your existing talent
and data quality. Second, expertise still matters, but you can go a long way by
hiring staff with Certified Analytics Professional (CAP) credentials and
encouraging existing staff to earn this data certification as well. Third, equip
your data team with high-quality data
to fuel their efforts.

Protip: That’s where master data management plays a vital supporting role in
creating a trusted data foundation.

Myth 4: You need a billion dollar data budget

Years ago, this myth was true. It used to be difficult and expensive to create a
data analytics department. That’s started to change. You already know that a
small army of statistics PhDs is not required. What else is wrong about this
myth?

There are three falling costs that make data analytics more accessible
in today’s market.

 1. The cost of data storage – especially in the cloud – continues to fall. An
    Infoworld analysis of cloud storage prices found that Amazon AWS, Microsoft,
    Google, and IBM have lowered their cloud prices.
 2. The cost of using data analytics software has come down. You no longer need
    to buy multiple software packages to achieve your goals.
 3. The ease of collecting more data than ever before through mobile devices and
    Internet of Things (IoT) platforms.

Add it up and you have a huge volume of data to execute a well-planned data
management strategy at a much more affordable price than ever before.

Myth 5: Data analytics requires a massive volume of data

“Big Data” enthusiasts love to speculate about what can be achieved with vast
databases. Sure, vast datasets play a role in the success of Google, Facebook,
and Amazon. However, that’s not the only way to go.

With just 1,000 records, you can carry out meaningful data analytics. For
example, if you had a thousand invoices from your largest supplier, you could
run a small analytics project against that data to look for patterns. Even
better, most organizations are already sitting on significant data resources.

Whether you have vast databases or not, the critical importance of asking good
data questions and understanding data quality will always matter.

Myth 6: Data analytics can improve every part of your business

“Data solves all problems! If only you give us enough budget and tools, we can
solve everything.” These kind of naïve statements gives data experts a bad name.
We’ve found that today’s data analytics tools are highly capable in some areas
and
weak in others.

Want to analyze an online marketing campaign? That’s an area where there are
many analytics tools available. Curious to analyze production, customer orders
or finance data? Those are also good areas to probe with data analytics.

Which manager should you promote to the executive level? What does Brexit mean
for your campaign? Should you cancel a struggling new product? Those are
questions that data analytics can’t solve. A focused approach to solving
critical business questions with a trusted data foundation is at the heart of
where companies are best served by a data analytics strategy.

Myth 7: Data analytics is only for online companies

While there’s an element of truth to this myth – tech companies have been among
the most successful adopters of analytics, for example ad spend (and the ensuing
data analytical insights) at Google and Facebook has generated billions of
dollars in revenue. But, what about the rest of us?

Data analytics might not be a key component of the products and services you
sell. However, data analytics do still matter as a way to enhance decision
making.

Data analytics methods are also critical in supporting continuous improvement
products. Just look at what Domino’s Pizza has been able to do by focusing on
creating a trusted data foundation to support their marketing campaigns.
Domino’s has transformed from a pizza restaurant to a technology company that
sells pizza!

Your Takeaway

Go into data analytics projects with your eyes wide open. Using tried and true
principles like master data management to produce clean trusted data as the
foundation of your data management strategy will enable your success.

If you regard data analytics as a powerful resource rather than a magic wand,
you will have debunked these 7 myths and truly feel the benefits with satisfied
subscribers of your data analytics program and nothing can dampen your data
enthusiasm then!

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HARVARD BUSINESS REVIEW ON THE PATH TO TRUSTWORTHY DATA


87%

of executives say that having a strong master data management program is
important to ensuring their future success.


66%

of executives believe their organizations are underinvesting in their
enterprise-wide data strategy.


73%

of executives say their organizations rely on more than 6 data types that are
essential to business operations.


74%

of respondents who have employed MDM say their organizations' approach to MDM is
moderately or very effective.

*Source: Harvard Business Review Analytic Services, June 2021

 1. 
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―Project Manager in the Finance Industry

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―User in Higher Education

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―Internal Consultant

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―Senior Manager Business Intelligence in the Servic

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―Vice President in the Manufacturing Industry

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"The best thing about the software is the UI, it is very nice and clear. It is
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―Administrator in Computer Software

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