In this special guest feature, Glen Rabie, CEO of Yellowfin, believes that while many analysts may fear they will be replaced by automation and AI, the role of the data analyst will increase in significance to the business and breadth of skills required. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics. Prior to starting Yellowfin, he worked in various roles at National Australia Bank including senior e-business consultant and global manager of employee self-service. Rabie holds a Masters in Commerce from the University of Melbourne.
introduction of AI, automation and data storytelling to the world of analytics
has not only had an immediate impact on the end users of analytics but also the
people that work in the field. While many analysts may fear they will be
replaced by automation and AI, I believe that the role of the data analyst will
increase in significance to the business and breadth of skills required.
analysts have traditionally spent a significant amount of their time doing
mundane and repetitive tasks like preparing data for analysis, creating reports
and dashboards then using these to manually search for meaningful changes in
their data. With traditional analytical
and business intelligence tools, analysts simply cannot explore every
combination or permutation of their data.
And if they do find something of interest, how do they determine if it’s
statistically relevant and of meaningful benefit to the business? The introduction of automated data discovery
addresses these issues. It reduces the
time to find insights, subsequently leaving far more time for analysts to add
value by interpreting their findings. This will require analysts to become
business savvy, (understanding the business, not just the data) and
storytellers with improved literacy skills to better communicate their
The role of
the data analyst today encompasses a broad range of data management and
analysis activities. These include
procuring, preparing, cleansing and modelling data, then creating reports and
dashboards through to bespoke analysis for the business to support decision
making. Of all these activities, the
true value to the business are those activities that are related to the
identification of critical changes or trends that impact the business and the
interpretation of that information to determine the possible impact to the
that business analysts face is that, although interpretation is the most
valuable activity that they undertake, it’s the one where they spend the least
amount of time. Most data analysts spend
only 20 percent of their time on actual data analysis and 80 percent of their
time doing tasks of little business benefit like finding, cleaning, and
modelling data, which is highly inefficient and adds little value to the
just data preparation that is inefficient.
The traditional tools for data analysis and visualization require a
completely manual approach for data discovery. Users must choose from a large
array of fields and filters and then slice and dice data in the search for
patterns, changes in trends and anomalies.
This manual process is incredibly time consuming, and highly prone to
human error and bias, especially in today’s data rich world. The result? The identification of critical
changes in business data is accidental rather than something that will happen
with certainty. This creates risk for
business leaders who want certainty in the data they use for decision making.
AI and automation
promise to radically change this paradigm.
Applied to analytics and business intelligence, many of the tedious and
time consuming processes will be done by machines. Smart data preparation that uses machine
learning to streamline the data profiling, matching and cleaning processes will
significantly reduce the time that analysts spend preparing data for analysis.
This in combination with AI driven data discovery, which applies a range of
sophisticated algorithms to data, will reduce time consuming data exploration
and the discovery of relevant business insights.
advances however, do not mean that AI will replace the data analyst. AI is
great for automation but it has fundamental limitations. Machines cannot
understand context. Only humans have the
capacity to contextualize data in complex terms such as the organizational
environment, external market factors, customer dynamics and much more. For instance, the ability to find meaning in
a downwards trend in product sales based on the anecdotal ramp up of marketing
by a competitor is far more than AI can process but it is relatively simple for
a human to do so.
of this shift will see data analysts spending far more time doing what machines
can’t – providing context and interpreting data. Data analysts will be elevated to that of
significant business partners, where they will use their data literacy skills
to assist the business to interpret the data, contextualize the insights
discovered and to tell compelling stories with that data. The result of this will be that the data
analysts of today need to become far more business savvy and build their skills
to develop narratives.
This does not mean that repetitive data analyst jobs will not disappear. For data analysts whose primary focus is the preparation of data and building of dashboards, their time will come sooner rather than later. However, organizations will rely more heavily on those with the skills to provide insight into what the data means. Data analysts will rely on AI driven tools that make the mundane aspects of their jobs easier, so that they can spend more time on highly valuable activities such as data interpretation and storytelling. As a result, they will able to provide meaningful analysis to the business to make better data-driven decisions.
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