By Abimbola Kareem
As coined by OLAP, “Business Intelligence refers to the technologies, application and practice for the collection, integration, presentation and analysis of business performance metrics for the purpose of supporting informed business decision making.” In reality, business intelligence helps business improve or compete in the market by leveraging their acquired – most times – proliferated, unutilized available data. This usually require organizations looking to grasp the opportunity posed by this practice, to be data driven i.e. being digitally supported by technology tools responsible for capturing and managing business operational and sales data.
It is imperative to note that – as a prerequisite to benefit from the capabilities of Business Intelligence – there is a need to transit from a traditional, crude, physical business practice to a digital driven business entity. This is because data would need to be seamlessly collected and processed to derive/compute actionable insights – relevant to business operations and performance.
The type of data ingested into organization’s business management tools and defined key performance indicators (KPIs) determines the insights generated on a dashboard. These insights can be descriptive, predictive or prescriptive in nature.
Business Intelligence actually provides descriptive, predictive and prescriptive analytics for business performance in order to guide business executive – especially strategic business stakeholders – to realign or adjust business operations and strategies towards attaining a set business goals. This is by leveraging historical data such as point of sales, inventory, operations, human resource, financial, production and any other data associated with business functionality. Note that no particular type of analytic is better than the other as they all co-exist or complement each other. For an organization to have a broader view of the market and the competition efficacy within their operating industry the following are required:
Descriptive analytics: aggregate data from various data sources by using ETL2 practice to provide insights into the past business performances and describes the implications of those performances. This sort of analytic is beneficial to businesses as it allows business executives to learn from past business performance, customer behaviors, product sales performance, and help organizations influence future outcome. Business metrics such as average money spent on customer acquisition, total stock in inventory, year-on-year change in sales. It helps improve the efficiency of organization’s production, operations, sales, inventory, finance and customer satisfaction and retention.
Predictive analytics: leverage statistical models and forecasting techniques to delve into the future business performance and predict the implications of such performance (what could happen to the business). This analytic assist organizations to have a view into what might happen to business in future. Although it cannot be 100% certain in its predictions as no statistical algorithm could do that. This is because the logic behind it is based on probabilities. It takes available data from a group of datasets and fill the missing data with best guesses. It identifies patterns in an aggregated data from organization’s business operations tools – such as Point of Sales Systems, Inventory Management Systems, Customer Relationship Management Systems, Human Resource Management Systems, Enterprise Resource Planning Systems, etc. – and apply statistical model and algorithm to capture relationships between various data sets. Predictive analytics can be used to forecast customer purchasing patterns, customer behaviour, identify supply demands, show trend in sales activities. It can be used by financial service delivery organizations to compute credit scores and determine the probability of customers making future credit repayments on time. Sales department can also use it to understand the probable sales performance at the end of an agreed period i.e. monthly, quarterly, yearly. While production department can use it to predict products that customers are likely to purchase, and the demography and geography of such customers.
Prescriptive analytics: this uses optimization and simulation algorithms to advise on possible outcomes and prescribe the implication of such outcomes (what the business should do). This sort of analytic prescribes – to businesses – a number of possible actions to be taken for a business improvement, competitive market standing, top-notch service delivery, etc. In reality, prescriptive analytic predicts what happens to business in future and why it will happen and provides recommendations in which the predictions can be well leveraged. The combination of techniques and factors such as business rule, machine learning, algorithm, and computational modeling techniques aids the feasibility of prescriptive analytics. For this to work, inputs from various datasets (real-time data feed, transaction data, big data, and historical data) are needed. This type of analytic can be used for inventory scheduling, customer experience optimization, production optimization
In order to optimize business operations and performance, there is a need for the capabilities of analyzing business related historical data to describe past (a minute ago to over 100 years ago) business performance and predicting possible future performance and prescribing necessary business directions to improve performance. Having this in place, there is a likelihood of realizing return on investment by the eventual reduction in the operating cost, increased revenues and improved operation performance and service delivery.
Benefits of Business Intelligence Systems
✔It delves into the past business data to describe what has happened to the business.
✔It leverages business data to forecast what could happen to the business.
✔It uses business data to prescribe actions to be taken and why they should be taken for certain KPIs.
✔It helps manage businesses based on available business data not personal emotions or assumptions.
✔It comes with a set of static and dynamic and interactive business reports – mostly, in form of dashboard visualization – for the consumption business decision makers.
✔It helps all aspect of business – provided they are data driven – to manage business performance and make informed decision in order to realign or re-strategize towards meeting organization’s business goals.
✔It helps in establishing customers’ behaviour by analyzing their psychographic (buying habits/ trend), demographic (age/sex/social status), and geographical data (location: urban, peri-urban and rural).
✔It helps organization provides transparency and visibility into its business activities.
✔It gives ROI on the acquisition and maintenance of data processing tools and infrastructure through which business data are collected, processed and stored.
- OLAP – Online Analytics Processing
- ETL – Extraction, Transformation and Loading
Abimbola Kareem is an IT expert with Swifta Systems and Services,Victoria Island,Lagos