Data standard in business analysis

This part of data in circulation that comes from social networks services is not, at least for now, in my area of professional interest. I don’t attach any importance to their standard; I consider such data an entertaining experience. Professionally, it’s different – that’s where standards matter. And this means data must meet quality as follows:

  • Must be delivered at appropriate times
  • Must be accurate
  • Must be valuable
  • Must be usable

Do we already have data meeting these standards? Great! Now we can work on a solution that will deliver our customer or end-user a value. I’m discussing value more broadly in the part concerning Business Intelligence.


Data sources, records, and storage

Currently, everything can be a source of data; whether gadgets bought in a shop (such as a fitness band, smartphone) or tools shared for free (like a web browser); everything from security cameras to highly specialized devices used for a particular purpose. Most devices connect to the internet, and therefore, they generate data for us.

Perhaps, it’s nothing new when I say that the primary form of storing generated data is saving it in appropriately prepared tables. When creating a table in a database, you need to remember the following three basic rules:

  1. If a table has lots of columns and rows, indexes facilitating searching for information are required – to improve the table’s performance.
  2. Data must be adequately prepared, and that’s what data modeling is about.
  3. The data model may take the form of a UML class diagram, with text descriptions of data units, classes, or objects – important for a domain or relations between those, and all that to ensure a common set of concepts for business analysis and implementation.

What happens with data next? We save it in databases, combine it in warehouses, and we use tools that allow us to count it as well as aggregate it so that finally, we can present it in a report in the user-friendly form of a chart or dashboard.

What’s important is that an analyst does not prepare data for usage – it is the development team’s responsibility to prepare the optimal code. An analyst has a task to determine the data source; they create a mapping, dictionaries, and provide context. To me, context is significant in programming development: it defines business needs and affects the way goals are achieved.

Analysts may, but don’t have to, be accountable for preparing data sets for imports. Such establishments are often formed during conversations with people who have knowledge in a particular business area. It’s good to differentiate a fact table from a measures table: the first one is a table with information, and the second – shows how to classify this information.

A system architect takes care of precisely determining where data should be loaded so that reaching a database would not freeze it. Whereas architects and DBAs (database architects) have their own tricks for making even huge data volumes operate efficiently. For that purpose, they use additional views, functions, and stored procedures.


Types of data analysis

When data is in a database, an analyst starts analyzing them. But, before doing so, they need to figure out what business expectations are and what information is supposed to be used for. In BABOK1 (Business Analysis Body of Knowledge), three types of data analysis are described. They are as follows:

  1. Descriptive analysis: uses historical data to understand and analyze past business performance; allows business information categorization and consolidation to best suit the stakeholder’s view. The business analysis focus is on the information and communication requirements for standard reporting and dashboards, ad hoc reporting, and query functionality.
  2. Predictive analysis: applies statistical analysis methods to historical data to identify patterns, and then uses that understanding of relationships and trends to make predictions about future events. The particular situations that are of interest to the stakeholders are specified, and their business rules are defined. The business analysis focus is on the information requirements for pattern recognition through data mining, predictive modelling, forecasting, and condition-driven alert.
  3. Prescriptive analysis: expands on predictive analytics to identify decisions to be made and to initiate appropriate action to improve business performance. Statistical optimization and simulation techniques can be used to determine the best solution or outcome among various choices. The business analysis focus is on the business objectives, constraints criteria, and the business rules that underpin the decision-making process.


Conclusions of data analysis

The results of data analysis should be patterns, findings, and information on dependencies. Such information can be used to create decision matrices, reports, or manager’s dashboards.

However, it must be remembered that access to big data sets, advanced toolsets and software to explore data may lead to an incidental violation, that is, a misinterpretation of data. Therefore, the requirements need to be verified and confirmed. I’ll write more about this in the next part of the series related to Business Intelligence. I strongly encourage you to read this.


  1. IIBA Babok Guide v3


  • Bartłomiej Janowski
  • Business and System Analyst
  • He has been involved in business analysis for 6 years. He has worked both for Polish and foreign companies, among others, within the service industry (employee benefits, insurance), and currently, he is involved in projects from life science. At work, he likes solving issues, and that’s why he willingly focuses on process optimization and searching for solutions supporting the needs of app users. With the team, he gladly shares his knowledge, experience, and… a good joke.

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