Distilling Data down to Actionable Details
It is not enough to implement systems and collect and store activity data. A key value proposition for the business is to justify the significant cost of Information Technology investments and one of those justifications one may be the ability to distill data down to actionable details that provide the business with timely insights management can use to make decisions that lead to profitable results.
The science of distilling data down to actionable details comes under the general topic of “Business Intelligence”. This article will discuss one of the key topics under Business Intelligence called Data Analytics and the need for Business Analyst to add this skill set to their toolbox.
Data Analytics is the means of distillation of data. IIBA’s CBDA, Certified Business Data Analytics, Certification is a pathway to learning the methods and standards used, by experts around the world, in applying the discipline of Data Analytics to producing actionable insights.
IIBA highlights four methods within Data Analytics that determine the type of insights one may generate. The decision to use one method or the other depends on identifying business research questions needing answers, the methods are:
- Descriptive Analytics, this method focuses on “what happened”, an historical and current view of events.
- Diagnostic Analytics, this method focuses on “why this happened”, what may have gone wrong suggesting the reason for success or failure in events.
- Predictive Analytics, this method focuses on “what may happen”, the data here is based on historical and current data and the use of prognostic modeling to predict a future event.
- Prescriptive Analytics, this method focuses on “what one can do to make a future event happen”, using predictive analysis to gain insights to results that provide estimates for different actions/result paths.
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Data is a foundational aspect to Data Analytics. Data collection and storage present a couple of common considerations, additional data preparation considerations are:
- Data Wrangling, which cover methods used to “clean and convert data” from one format to another so that analytics tools can use it.
- Data Extraction, which involve “identifying and capturing the data needed” to answer the outstanding question(s).
- Data Preparation, which involves “preparing that data for easy use”, highlights several points, they are:
- Importing, the relevant data sets,
- Cleaning the data, by removing out of range data, duplicate data, stray data,
- Transforming the data, treating missing values etc…..
- Processing the data, by preparing data for analysis by parsing, concatenation etc..,
- Logging the data, by describing the data sets used, metadata details, data sources used, collection methods used, so others may discover reuse potential and as evidence against generated insights,
- Backing up the data, so clean versions of the data are available for reuse,
Some hidden benefits of data preparation are that it raises the quality of data so analytics tools can consume that data. As well, prepared data is no longer siloed within a segment of the organization limiting access, rather is available to all authorized users in a reusable form going forward.
When the data is ready, understanding what business research questions are needing insights is key, this will lead to the choice of data analytics method to select that will support the insight model to use that will in turn generate the timely actionable insights the business needs to make decisions.
In conclusion, Business Analysts are increasingly encountering the need for skill in the topic of business intelligence in the form of implementing data analytics solutions in the normal course of work. IIBA’s CBDA certification is a strong first step to gaining the skill to apply data analytics tools and methods. This skill to design and implement processes that generate timely, evidence based, actionable insights management can use to make informed decisions is very valuable to organizations.