Look at Big Data ROI from all different Angles.
Quantifying the
impact of analytics in some form should always be possible. Total investment
on Analytics and incremental revenue generated by analytics…can lead to ROI on
Analytics. BI solution
should be based on a quantitative and analytical method at the same time. Because
an analytical method allows you to assess economic, financial and technical
impact of an investment, and the method must simultaneously be quantitative by the nature of the problem. Also pay the attention to the need to make
big data analysis simple, fast and easy for all business people to use. In terms
of ROI, it could be impactful and measurable when you apply big data style
analysis to operational decisions – the thousands of decisions made each day at
the frontlines of business.
The low ROI may be
related to ineffective or failed enterprise analytics. The problem is threefold.
1).You must have the experience/and competence in analytics and handle the
data, to retrieve the information caught in the data. Even if you are
successful you are left with problem 2). Be able to understand what to do with
the result. How you can capitalize on it and put it into process. 3)
Define the set of KPIs to measure the ROI
ROI measurement is an
incremental process that changes as new variables are included in its
calculation. The closer you get to the 'real' ROI the better for the
business. BI tools can be used
to uncover business deficiencies when their results are made actionable and they
are most valuable when they are part of a holistic approach to addressing
business challenges. Get the ROI of
an investment, an opportunity cost, an appropriate amount of investment, are
part of the economic analysis. Better implementation of the economic analysis
to business arises from the linear programming algorithm to non-linear, or
optimization in its different forms. Because you not only get an optimal
solution, but the dual of it.
Measure ROI on a BI
tool investment: How can you measure ROI on BI Investment tool where a BI
solution directly helps organizations in making their decisions based on facts?
The intelligence provided by that tool needs to be made actionable. Then you
will need to collect the results of those actions in the form of gains or
losses. A timeframe needs to be set to gather incremental results and measure
ROI as a projected result. At the
end of the timeframe, you would have to subtract the gains/losses from the
investment cost
There may be many
other factors to include in the 'before and after' measurements, and that
was a basic approach. From a practical point of view, you need to start with a
set of data elements that are already available. For example, if there was a
marketing effort underway for a new product, all costs incurred in every
channel should be included in the final calculation of Net profit and ROI. If
some of those costs cannot be originally captured, those calculations will be
skewed. This means that each of the ROI measurement presentations to Management
needs to include what variables were used in the analysis.
Review the
BI/Analytics/Big Data investment portfolio holistically via the effective governance
principles. Many "big data" experts claim that they can't compute
ROI because what they are doing is too important to have a valid control group,
it is obviously not true. Review BI/analytics PPM holistically, one can get
much higher return on investing in BI itself. If investment in BI has given right returns, then implementing a
right Big Data Landscape should give very high return. Expand the architecture to next level - if BI implementation has
not given the return than look at the issue - either something to do with
vendor or base organization is not flexible to change
ROI directly linked
to the saved loss if the firms trying to initiate an analytics project for the
first time - Usually an analytics project is taken up by a company
when they face a business challenge. And a problem becomes a business challenge
when the company is facing some kind of loss, negative returns, wastage or not
meeting its targets etc. Averting this challenge will be one of the prime
objectives of a project. The ROI for such cases should be directly linked
to the loss arising from the initial problem and identify cost of hiring a team
to mitigate this loss. ROI = (Saved loss)/Cost incurred for analytics
Companies track the accuracy of the analytics model and plan the entire value chain based on the forecasts for ongoing Analytics projects in a company, such as forecasting demand. ROI for such projects cannot be directly computed. However, ROI improvement for other departments of the company will become the ROI for the analytics projects as the improvement is caused by the prediction accuracy.
Companies track the accuracy of the analytics model and plan the entire value chain based on the forecasts for ongoing Analytics projects in a company, such as forecasting demand. ROI for such projects cannot be directly computed. However, ROI improvement for other departments of the company will become the ROI for the analytics projects as the improvement is caused by the prediction accuracy.
ROI is more based on
"impact" and "urgency" calculations when measure BI
projects against other BI projects for prioritizing and the ROI determines
which goes forward. That is, you decide how great the impact of an implemented
solution is based on number of users that will benefit, time saved, dollars
projected to be raised as a result of the implementation and the other factor
being timing of when the organization needs the solution
Analytics ROI on
Productivity: Productivity gain does not guarantee savings and some
business areas may be reluctant to report this gain due to fear that it will
result in request to reduce staff. But with growing data volume and demand for
BI, most business areas have unmet demand and would be eager to have their
staff shift their time so they spend more time on value added work such as analyzing
information rather than finding and assembling it. It works best when ROI
metrics are positioned this way.
Big Data needs to have a reasonable ROI, however, always keep in mind, Big Data benefits for both incremental business improvement and long term business growth, ROI number is just one dimension to assess Big Data investment, but not the only one.
Big Data needs to have a reasonable ROI, however, always keep in mind, Big Data benefits for both incremental business improvement and long term business growth, ROI number is just one dimension to assess Big Data investment, but not the only one.
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