If You Have a Business, You Need to Understand How to Curate Business Intelligence
Every business has data. Whether that data is collecting virtual dust in a warehouse, sitting in a filing cabinet, in the cloud, or hosted offsite, the data exists. Why is this important? Like most businesses, you’re likely interested in increasing revenue, efficiency, employee satisfaction, gaining a competitive advantage – the list goes on. What do all of these have in common? Data.
Business Intelligence is accessing, analyzing, and refining available data to extract reliable conclusions and make informed business decisions. These decisions can range from what a company needs to improve employee retention all the way to preparations for taking a new product to market, and what strategies are involved. It’s all in the data.
One way of looking at business intelligence is in building blocks. These building blocks contain better insight into what makes up business intelligence, and how your business can utilize its data to better address specific needs. Below I will expand on six major components to building an effective business intelligence strategy.
Establishing Strategic Support/Goals and Securing Access to the Data:
An important starting point to business intelligence is accessing the data. Strategic-level support is required for any successful business intelligence project. There needs to be support at high levels in the company to give the business intelligence support team access to the data, people, and processes needed to properly capture and profile the data. Without proper access to the needed resources, a business intelligence system cannot be developed to accurately reflect what is happening in the company.
After establishing support for accessing the data, it’s important to set business intelligence goals. For example: the business intelligence system will be the sole location for reporting and analysis of business performance. Data, calculations, and processes will be fully defined in a clear and understandable manner. The point of a strategic goal is to lay out the high-level target results that can be used to judge the overall success of the business intelligence process.
Preparing Data for Consumption:
As I have established, every business has data. But where is that data stored? Easy answer; it’s stored somewhere. Data storage models can be as large as structured data warehouses, cloud-based data lakes, small departmental DataMarts, or direct-fed reports/dashboards from source systems. No matter where the data is stored, it is important that it is prepared for consumption.
In the case of a data warehouse, the warehouse should be the sole version of the truth for a company. All data in the warehouse should be properly validated to return accurate results. Without quality information, the results will not reflect what is really occurring in a company, and the business intelligence process will fail. It can be a great benefit to an organization to use outside help in ensuring a data warehouse is reliable and valid.
Executing the Extract Transform Load:
Now that you have validated that your data is reliable and prepared for consuming, your team should move to prep the data for extraction. This is called the Extract Transform Load (ETL). ETL is the process of extracting data from your source systems, transforming the data into a unified format, and loading it into your data store.
Next to a well-designed warehouse, the ETL processes are critical to success. The processes set up here guarantees that the data you are viewing is consistent and proper no matter the source systems. A good example is customer data. Let’s say you have an Enterprise Resource Planning (ERP) system and a Customer Relationship Management (CRM) system. Each has its own customer list and there are differences in the customers. ETL processes will unify those into a single customer list by a set of defined business rules. One of the benefits of a good ETL system is its ability to find data errors in the source system that need to be corrected and report those back for updates.
Applying an Analytics Engine to Comprehend Your Data:
Your business data has been extracted, so what happens next? You want to read this data in a comprehensible manner, which means you need an analytics engine. Analytics engines are used to model subsets to complete information stored in the data store. As an integral part of business intelligence, they allow reporting and analytics to be performed against a data set that is normally too time consuming to be done in a warehouse model.
Analytics engines are designed and built for superior query response times and the ability to support ad hoc query requests in a more efficient matter. The efficiency in the ad hoc query approach is created by the engine design, which allows for a larger number of attributes to be used to slice the data without specific tuning needed to return the results. Another benefit is the ability to define complex calculations directly in the data model. The calculations are built to allow an end-user to slice the data in more ways than what may be envisioned in the initial design, all while still providing proper results.
Visualization Allows You to Present Your Data:
So, you can comprehend the data, but you want to represent the data in a way that people in your company can observe, browse, and understand. This is called visualization. The visualization portion of the process takes the data in your data store and allows end-users to act on it. Visualizations fall into these three different categories to solve different types of issues:
- Tabular Reporting: The standard legacy reporting model where data is represented in rows and columns, usually with a chart or graph to represent the data in a graphical manner. Navigation and view of the information are tightly controlled and a limited amount of exploration options are given to the users. Most data consumed in this model are more desktop/print-oriented and do not translate well to a mobile display.
- Dashboards: Data is generally presented in a very visual format and is focused around Key Performance Indicators (KPIs). These KPIs give a quick overview of the health of a process or processes in the company, and if things are on track. Users can then drill down on these values to get to more and more detail, finding the root causes of problems where they exist. Most dashboarding systems are desktop and mobile capabilities and can deliver actionable information in a short time frame.
- Self Service Business Intelligence: The area of self-service business intelligence is a growing area that lets end users access and work with business data, even if they do not have a background in statistical analysis, business intelligence, or data mining. Analytics models are an important part of self-service business intelligence as they can help make the data more accessible and consumable by a larger audience. End users with these capabilities can greatly enhance the benefits that are derived from a company data store because they generally know what questions need answering. Overall visualizations take your data from a collection of data points and turn it into actionable intelligence to help improve the overall business. Visualizations make the information consumable for all levels of your organization.
Using Predictive Analytics to Make Informed Decisions:
Predictive Analytics is the final block of business intelligence. Predictive Analytics, in its most basic form, is looking at past trends to predict future performance. Predictive Analytics use many different tools, such as data mining, artificial intelligence, statistical analysis, machine learning, and predictive modeling, to process the data and help determine future outcomes. The end goal of any predictive system is to remove the situation of, “we believe that selling these products together results in better sales,” and changes it to, “based on past observed performance, we believe selling products with these attributes together will result in better sales”. Predictive analytics is targeted at finding deep, hidden trends and patterns in the data to give us a better understanding of what is going on and predict how future changes will occur with a higher degree of confidence and success.
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