Data Analytics and Planning
The
Importance of Data Analytics in Planning
Planning
in a Data Driven Business
Humanity used to think of the
world as a mystery place with abstracts, feeling oriented subjects and
uncertainty. Now, a significant proportion of us see it as numbers and factors.
Today, there are about 2.5 quintillion bytes of data created each day according
to Forbes[1]. In the other word, it’s more than 320
million bytes generated everyday on average by each person on earth right now.
That’s for Internet, Searches, Social media and Communication, that what’s
about business alone? Research tells that the average company manage 162.9 TB
of data in 2016[2]. Although it’s varied between big and small
businesses, it is a huge amount of data to be managed and used properly for any
entity.
We have been hearing a lot about
how “data is the new oil” but we do not hear much about any fancy vehicles or
machines running with that new fuel. It is because we are still in the process
of defining the “vehicle” or “machinery” and its engines (Machine Learning, AI
Algorithms…). The fact that 60% of global managers and leaders think that half
or more of their organization’s data is dark[3] or in the other
word, left unused tells a lot. In short, the question is “How can business take
advantage of their data reserve and what should they do to enrich the ‘reservoir’?”.
There is one clear usage of data is to plan, including both Operation Planning and
Financial Planning.
Business shapes data and Data Drives Business
A Hospital manager can say that having more accepted insurance
providers will encourage customer to visit the hospital. A Real Estate Salesman
can say that growth of income in some residential area will lead to more Real
Estate deal closed. But who is there to prove that? How do we really measure
the impact? One straight up answer is that it must be proven and measured by
historical numbers and related facts.
A traditional planning method involves a lot of assumptions
and drivers without being proven by numbers and facts. In the age of
information, this might no longer be the case. With the help of data analytics,
business’s hypothesis can be drawn and validated with ease.
How Data
Analysis can influence planning and how planning can shapes data
There are 4 main types of analytics: Descriptive Analytics,
Diagnostic Analytics, Predictive Analytics and Prescriptive Analytics. Out of
which, Predictive Analysis of data is the most commonly utilized type of
analytics[4]. The first two focus on giving comments on what’s
already happened and finding out the root cause while the last two give a
forward-looking perspective of what might happen and which actions can be
performed to achieve strategic targets. On a typical planning point of view, a
business can identify their problem by take a look at the presentation of
relevant data (Descriptive), then draw out their hypothesis to rationalize the
problem, subsequently prove it by data and number (Diagnostic). Using the
results and factor contribution, planner can predict their future performance
giving the change in the proven relevant market condition and drivers
fluctuation (Predictive), on which, decision makers can also put the action
required or sufficient changes to improve upcoming results (Prescriptive).
To put the process into a context, we can take a look at a simple
example. CAPEX Investment is a big part of every Hospital Financial Planning, however,
it is very difficult to know which types of equipment a hospital should
purchase to maintain the equivalent service level while still being
cost-effective. By analyzing the popularity and correlation between multiple
factors of visitors such as related condition/disease, visited specialty,
treatment procedure and equipment required… (Descriptive), we can measure the
impact (Diagnostic) and predict the case mix, by which derive number of
equipment needed to satisfy respective service level (Predictive). At the end
of this, planner should be able to prepare their purchase requisition for
future period, both short term and long term (Prescriptive).
There is no fixed sequence of using different types of
analytics. Mixing all of these types can help business boost its planning
values in multiple areas.
Data Analysis can streamline the planning formulation. By
leveraging the help of automation, Data analytics can collect, aggregate and
process data seamlessly. On the summary perspective, high level executives can
control the centralized plan, track performance, and produce reports while
planner can prepare the plan with baseline and proven facts from historical
data on the detail perspective. Critically, both of them work on the same set
of data ensuring a single version of truth and real-time integration.
Data analysis can identify savings and efficiencies. Data
analytics offers decision makers productivity tools to mitigate impact of
previous year problem and take respective measure to tackle it. By monitoring
planning achievement and real-time performance indicators, prescriptive
analytics can help business identify problem as soon as it arrives or even
before that, thus, planner can put the right amount of resources on what is
important. In the other hand, budget monitoring and forecasting which is more
relevant with real-time data can help business trigger immediate action based
on market condition.
Data analysis can improve the efficiency of operation and
finance planning. Thanks to the help of technology, data collection and
aggregation has never been easier. As a consequences, Operation and Financial
planning are being pushed together tightly. Financial Planner now use a lot of
operational data for their forecast in combination with their existing
financial indicators while Operational Planner can optimize results of
financial planning to drive operation toward an accurate management
expectation.
The starting point
In conclusion, business should be enthusiastic toward a more
data centric culture. Although Big Data and Machine Learning have brought the
gap between planning and analytics closer than ever, planning is still heavily
relied on human’s activities. In estimation, only 33% of the work activities
under Optimization and Planning can be automated in 2016 according to McKinsey
Global Institute[5], therefore, the story will not only about
changing the infrastructure but it is also about equipping management and
employees with necessary knowledge to embrace the future of data analytics and
planning.
[1] How
Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read
[2] 2016
Data & Analytics Survey
[4] Data
and Analytics - Data-Driven Business Models: A Blueprint for Innovation
[5] McKinsey
Global Institute - THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD
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