Written by Timothy Figueroa . Published in the article.
It’s not enough just to implement a system and collect and store activity data. A key value proposition for a business is to justify the significant cost of investing in information technology, and one of those justifications may be the ability to distill data down to actionable detail. . beneficial results.
The science of distilling data down to actionable detail falls under the general topic of “business intelligence.” This article discusses one of the key topics in business intelligence called data analysis and the need for business analysts to add this set of skills to their toolbox.
Data analysis is a means of extracting data. IIBA’s CBDA, Certified Business Data Analytics, Certification is a pathway to learn the methods and standards used by professionals around the world when applying the field of data analytics to generate actionable insights.
IIBA highlights four methods within data analysis that determine the types of insights that can be generated. Which method you use depends on whether you want to identify a business research question that needs an answer. Method is as follows.
- Descriptive analysis, this method focuses on ‘what happened’, a past and present view of events.
- Diagnostic analysis, this method focuses on “why did this happen?” and shows what went wrong suggesting the reasons for the success or failure of the event.
- Predictive analytics, this method focuses on “what will happen”. The data here is based on past and present data and uses predictive modeling to predict future events.
- Prescriptive analytics, this method focuses on “what could be done to bring about future events” and uses predictive analytics to provide insight into outcomes that provide estimates of different action/outcome paths obtain.
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Data is a fundamental aspect of data analysis. Collecting and storing data has some general considerations. Additional data preparation considerations include:
- Data wrangling describes the methods used to “clean and transform data” from one format to another so that it can be used by analytical tools.
- Data extraction, which involves “identifying and capturing the data needed” to answer open questions.
- Data preparation, which involves “preparing that data for easy consumption”, emphasizes several points. They are:
- import, related datasets,
- out-of-bounds data, duplicate data, stray data,
- Transforming data, handling missing values, etc…..
- processing data by preparing data for analysis by parsing, concatenation, etc.;
- By describing the datasets used, the metadata details, the data sources used, and the collection methods used, we can log the data so that others can discover potential reuses, generate as evidence for the insight
- Back up your data so you can reuse clean versions of your data
Some of the hidden benefits of data preparation are improving the quality of the data so that analytical tools can use it. Similarly, prepared data is no longer siled within organizational segments that restrict access and is now available to all authorized users in a reusable format.
Once your data is ready, it’s important to understand the business research questions that require insight. This leads to choosing a data analysis method that supports the insight model you use to generate the timely, actionable insights your business needs. decide.
In conclusion, business analysts are increasingly faced with a need for skills on the topic of business intelligence in the form of implementing data analysis solutions in their normal course of work. Her CBDA certification from IIBA is a strong first step towards acquiring the skills to apply data analysis tools and methods. This skill in designing and implementing processes that generate timely, evidence-based, and actionable insights that managers can use to make informed decisions is invaluable to an organization.
