Insights into the World of Business Intelligence and Workforce Analytics: 10 Things to Know

May 16, 2016
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Business Intelligence, Business Analysis and Predictive Analysis are the pillars of workforce data analytics. While the former was extracted out of automated reports, BA was actually a brainchild of user experiences, workflow discussions and multiple processes. Finally, Predictive Analysis is the more of a layered approach— involving aspects related to data mining, predictive modelling, risk analysis and other innovative advances.

Business Intelligence, Business Analysis and Predictive Analysis are the pillars of workforce data analytics. While the former was extracted out of automated reports, BA was actually a brainchild of user experiences, workflow discussions and multiple processes. Finally, Predictive Analysis is the more of a layered approach— involving aspects related to data mining, predictive modelling, risk analysis and other innovative advances. Admittedly, BI, BA and PA are the powerful tools which are capable of data discovery, meaningful interpretation and simplistic communication— across multiple channels.


Figure 1: Business Intelligence Tools for Accessing the Data Warehouse

Most Business Analysts handling BI would often pitch in with SMEs— flaunting the non-technical aspects of data handling. Predictive analysts, however, are the usual data scientists— working with statistics and data mining. Most data developers usually include these concepts while handling the intricacies of derives, transforms, nodes, copies and display data— segregated across a wide-array of formats. It is therefore imperative to clear out the concepts of analytics and Business Intelligence— instrumental in handling supply chain management, assessing varied pricing modes and analyzing statistical data.

  • The Raging Trend of Self-Service BI

Modernized form of Business Intelligence expects us to offer something which is intuitive and self-explanatory. For example, iTunes is one database which offers self-service when music selection is considered. The interface is independent of customizations and data mining can therefore be initiated with precision. Intuitive spells of Business Intelligence are also exhibited by streaming apps like Showbox — capable of modifying themselves at the smallest of requirements. Narrowing down on data is easy and this form of BI should be prioritized, almost immediately. Self-service analysis is quite popular with modern day entrepreneurs and growing in stature with each passing day.

  • Emergence of Alerts

Detailed reports are good only when descriptions are needed. However, in case of time constraints— a message, text alert or even an emoji should suffice. Messages come in with their own codes which are way simpler than detailed reports. Vintage practices aren’t suitable anymore with the evolution of modern day BI tools. In the present era, smartphones offer a lot of possibilities and it is easier to include them as potential BI Tools.

  • Keeping up with Descriptive Analytics

Alerts are fine when the event descriptions need to be avoided but in most cases, departmental data is asked for. Descriptive analytics act as rearview mirrors— offering insights into the modus operandi of any process. These include reports and anything that is detailed— enhancing the analytical maturity.

  • Looking Beyond Operational Systems

Most of the previously encountered data systems were highly dependent on the software and operational models. These included homegrown hierarchies, SQL servers, Oracle, DBMS systems, MySQL and a slew of open source engines. Data silos, featured subsets and shared files were also encountered but the modern day BI tools are much more than those character based data formats. Be it images, videos or social media threads— things have improved due to a multitude of integration processes. Other than government implied datasets and varied industry sources— data has certainly moved in a different yet positive direction.

  • Understanding Competitive Intelligence

Competitive intelligence is one integral part of BI analytics as it helps companies gather information about their competitors and analyze subsequent steps, accordingly. While Espionage is one black-hat technique suited towards data procurement, competitive analysis is the approach which needs to be encouraged.

According to the basics of Business Intelligence, Competitive Intelligence completely depends on the nature of competitors and the questions which need to be asked— in order to gather the best possible information sets. Companies can either hire professionals or extract information from marketing blogs, site materials, articles and obviously Search Engines

  • Looking back with Diagnostic Analysis

Once the datasets are identified and processing begins— it’s only a matter of time we come across failures. This is when diagnostic analysis comes in play— allowing us to reflect back up the reasons and take actions, accordingly. Here is a process that involves peeping into the realms of metadata.

  • Data Visualization and the Perks of it

Visualization is certainly a handy tool when data trends are searched of. Instead of searching an entire column of files and characters— visualization can help segregate the needed entities with ease. It brings in the concept of augmented reality and gives us the freedom of choosing the desired operating platform. Moreover, it is possible to bring in application interfaces and design a wide-array of developer programs with Data visualization aboard.

  • Probabilistic Predictive Analysis

This aspect of enabling BI tools offers reliable insights into the expected modus operandi. PAs can look into the probabilities and bring out data, accordingly. With the outcome predicted on the basis of probabilistic means, data quality is bound to improve. For example, an ecommerce website is best served by this tool as the expected search can always be predicted.Another justification to the same is Showbox Apk which can understand the futuristic preferences of customers and modify the interface, accordingly. This usually happens over a period of time, as the firm uses the statistical data, via regression analysis.

  • Pervasiveness of IoT

Typical systems lacking the desired BI don’t usually bring in the concept of IoT— something which the newer models have adopted. An example would be the emergence of driverless vehicles, offering on-board sensors which can communicate without any physical contact. IoTs usually offer data streams in real-time but the noise data needs to be eliminated.

Figure 2: IoT as a concept is immensely popular among the millennials

  • Theory of Perspective Analysis

With BI tools and analysis, it is often difficult to determine the course of action. Perspective Analysis can regulate the approach that needs to be taken while working with data optimization and simulation models. This eliminates multiple risks and encourages forecasting.

  • Enrich Internal Data with Intuitive Analytics

Cloud computing has experienced revolutionary changes over the years with Software as a Service being the primary offering. Things have changed even more with multi-tenant vendors coming in play. One such example is IBM’s Watson which is a functional service, offering loads of bankable on-board data clusters to work with. This in turn enriches the internal, organizational data streams— using RESTful APIs for beefing up the clusters with computing prowess. Using efficient analytics operation, we can surely convert our data sources into currencies.

Bottom Line

Changes in a business can sometimes be overpowering and overwhelming but with the growth of Business Intelligence tools and analytics, it is now possible to measure the potency of ramifications and improve upon them.