Time value of business insights
Business insights extracted through data mining are valuable for a very short period of time
Business insights extracted through data mining are valuable for a very short period of time
Just because your organization has Saleforce.com or an Oracle database doesn’t mean you should be going out and hiring a team of data scientists to work full-time. The value of insights that these professionals generate, tends to diminish over time if no new data-sets are being populated or expanded as a result of growing business operations.
Data-sets, like gold mines, are finite and vary based on the three V’s (volume, variety and velocity). Looking from an organization’s leadership perspective, insights discovery typically takes place in four stages.
Initial Stage (Finding the gold mine)
Data Discovery
Decision Making
Monitoring
The value an organization gets out of data sets varies over time, but for most typical engagements the process is very similar. The figure below is an abstract view of how much value is generated over time (Note that this figure is not cumulative) during each of the aforementioned stages.
Finding the gold mine
Today most companies are sitting on Enterprise Resource Planning (ERP) and CRM Customer Relationship Management (CRM) tools that over time become data gold mines. Even small tools like Quickbooks, IBM Rationale Clearquest and Clearcase are storing away valuable log files that can unlock tremendous efficiency gains for organizations. The problem is that most organizations do not realize, or even understand, the significant value their data-sets can generate for them.
In this first stage, while no value is extracted from data-sets, an acknowledgement has to happen by the organization’s leadership recognizing the value of data. The leader then assembles a team of business analytics and data science professionals that are asked to explore the possibility of finding golden nuggets in these data sets.
There are many different ways data brings value to an organization: transparency, platform for experimentation, customized actions, and automation of decision making. A right combination of skill sets is also required to analyze this data.
Discovery Stage
Following the formation of a team, Data discovery is the phase where a number of processes happen. Business analysts look at the data and try to determine what analytics frameworks should be applied to the data; what Key Performance Indicators (KPIs) to extract; and what potential business cases can be constructed. Business analysts and scientists are not only examining the data but also determining the best ways to perform robustness and data hygiene checks.
Once some inroads are made into data structure and cleaning, rich insights and trends start to emerge. Many insights at this point are interesting but less are useful and actionable. This stage is where logged data is converted to daily active users, transaction amounts per day, useful sales ratios and histogram plots of various segments.
During this exploratory stage, a lot of visualization tools become useful as well. Time series and historical analysis are conducted to see how things are changing over time. Predictive analysis can be done to see where the organization is headed. In summary, this stage brings a tremendous amount of transparency to leadership uncovering opportunities for data to play a role.
Decision Making
After a significant amount of brainstorming with data-sets, analysts and leaders define a clear framework that separates out vanity metrics from ones that are useful and actionable. This is an important step because two very important things happen: (1) determination of what data the organization needs to consider, and more important, what data it does not (2) putting together a system in place that determines the right intervals to repeat the insight generation process.
Determining what metrics to focus on is a very critical step because it ties analytics to the organization’s strategic plan. A connection to the strategic plan also means that leaders of the organization have to openly assimilate the metric into the goals of the organization. The framework becomes important because it helps members of the organization understand how they contribute to the goal.
Another important aspect at this stage of the process is the development and deployment of data visualization that is accessible by all members of the organization. This enables democratization of data and allows everyone to be on the same page.
Monitoring Stage
After a visualization tool has been setup, organizations start to monitor their data. The organization also gets aligned with a certain analytical framework. Leaders get updated on the statistics of data periodically, and they take actions when things get out of trend. The amount of value being generated at each data generation cycle is at best incremental and data scientists and analytics don’t have much more value to add besides making sure data updates are accurate. Organizations that become smart enable reporting by exception.
CONCLUSION
Hiring a data science or business analytics team makes sense if an organization is going through tremendous growth and continues to generate new data sets. If this is not the case, data scientists are only valuable for a very small stage of the insights generation process. As a leader of an organization not undergoing tremendous growth, you must carefully think about your goals and the depth of goal mine you think you are about to mine
Originally published at ehasan.com.