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Business Intelligence (BI): Unpacking the conundrum
Dennis Chindove

Business Intelligence (BI): Unpacking the conundrum


Today’s decision-makers are faced with the necessity of making big business decisions faster and more accurately than ever. Fortunately, the right strategies and tools can help you make data-driven decisions quickly and accurately.


What is Business Intelligence (BI)?

Business intelligence (BI) is the collection of processes, technologies, skills, and applications used to make informed, data-driven business decisions. BI includes data collection, data aggregation, analysis, and meaningful presentation that facilitates decision-making.


Data-driven organisations use a variety of BI tools to access historical and real-time data in a data repository to perform queries, generate customised reports, and predict future trends. These tools include advanced analytics performed by trained data scientists as well as insights generated autonomously by machine learning algorithms.


Data repositories for BI applications include data warehouses (centralised or decentralised), production databases, operational data stores, and data marts.


Business Intelligence vs Analytics

BI is often confused with business analytics. Business analytics (BA) refers to statistical methods used to measure performance and optimise business processes.


How do you differentiate once and for all between Business Analytics and Business Intelligence? Read below how both can be critical to success.


Today when we talk about Business Intelligence and Business Analytics there tends to be confusion, especially for those who are just beginning to understand the concepts and how to apply them. The distinction between Business Intelligence and Business Analytics is critical to its applicability in business.


Both approaches are on the edge of a major shift and are geared towards providing a broad insight into business information. Likewise, there is a growing emphasis on superior tools and more advanced software in the making of decisions. Business CEOs often admit that business analytics (BA) walks alongside BI.


Although they are distinct, the two tools are connected. Business intelligence provides a way to accumulate data to find information primarily through questions, reports and online analytical processes. The only problem is that the standard BI tools are not very flexible and most databases are not designed for sudden changes.


On the other hand, business analytics takes advantage of statistical and quantitative data for explanatory modelling, acting as a compass to organise risk measurement and predict results. The result of this adhesion is an aggregate perception of business information, creating a new type of intelligence, capable of optimising the decision-making process within the organisation and capable of responding to the inherent demands in ever more complex and competitive scenarios.


BA is a natural advance of the information aspect obtained from BI since organisations need to obtain increasingly efficient answers. They do it based on a proactive approach, that is to say, through analytical functionalities and predictive models that take as a base historical information to anticipate future trends.


We can compare Business Intelligence and Business Analytics with a rear view and the GPS respectively. A rear-view mirror shows the past with its data trying to predict the future, and a GPS uses predictive mechanisms such as analytical techniques and statistics to identify trends and probabilities of knowing how the future is – by telling you how to move forward.


Data analytics is the process of analysing sets of data to gain insights. Two types of data analytics are:


  • Predictive analytics — analysing historical data to determine the most likely outcome.
  • Prescriptive analytics — running hypothetical scenarios to determine the most likely outcome of a certain action.

Data analytics is a primary component of BI and BA, but only one part of the overall system.


BA is a similar yet separate process with a different function. BA mines historical data for trends and insights to drive business change. BI uses historical and real-time data to enable decision-making in the present: i.e. evaluate what works, what doesn’t then decide how best to move forward.


BI primarily helps run a business today. BA is primarily used to predict what will happen in the future.


Why is Business Intelligence Important?

The growing accessibility of big data is making modern business decisions more crucial but also more difficult to obtain. An enterprise data warehouse often contains a terabyte or more of raw data that needs to be processed and made ready for analysis. BI systems allow for comprehensive analysis of data—often in minutes—to respond to specific business requests.


For example, SKF, a global manufacturing enterprise, need to be able to accurately forecast the size of the market for its products and the demand for specific product types.


"What products the company should produce and in what volumes? Where to invest or divest and how to react to emerging industry trends? Talend is helping us do that."—Fritz Ulrich Dettmer, Manager of Business Intelligence, SKF.


F+W is a content and e-commerce company dedicated to innovation and creativity. That means their entire team needs to be able to access the data necessary to evaluate success and drive progress.


"With Talend, our cloud and on-premises systems are now speaking together. This has empowered the organisation as a whole and is moving us from making decisions based on gut feel to making them based on consistent data."—Greg Sitzman, VP of Business Intelligence, F+W.


Other key benefits of BI include:
  • Accelerated time-to-answer — In-memory analytics with cloud-based data warehouse solutions can analyse data in real-time, providing fact-based information in minutes.
  • Better business decisions — BI extracts facts and transforms data into actionable information that can be trusted.
  • Improved operational efficiency — BI makes the interconnections between different components of the business more visible, so problems and inefficiencies can be identified and dealt with more quickly.
  • Increased ROI — BI helps identify resources needed to reach goals, increases productivity by making data analysis quicker, and aids in the discovery of new revenue streams.
  • Faster reporting — BI provides real-time reporting of up-to-the-minute, accurate datasets giving organisations a competitive edge in solving complex business problems.
  • Accurate strategies — BI helps identify important trends and patterns in data that can be utilised to set priorities and allocate resources to meet desired goals.
  • Satisfied customers — BI provides data on KPIs that improve core business functions (improved product or service, a decrease in time to market) resulting in higher customer satisfaction scores (CSATs).
Business Intelligence Tools

BI can be separated into two main categories: traditional BI and self-service BI. Traditional BI is handled by an IT team or data specialists who run queries, provide guided analysis and create reports. The downfall with this approach is that it can take weeks or longer to prepare a report.


The main push today is for, “self-service business intelligence (SSBI).” Self-service BI is when business professionals, without any training in statistical analysis, make queries and generate reports—ad-hoc analysis—often by using interactive dashboards installed on a PC. These tools are intuitive, user-friendly, and provide access to data in real time.


Business Intelligence Tools: 7 Key Features and Functions

Efficient business intelligence requires the right tools. Different types of BI tools perform various pieces of the overall BI process, and function according to different standards. They operate as standalone tools or as part of an integrated suite of products.


  • Online analytical processing (OLAP) — BI tools that are used to analyse large volumes of historical data with drill-down functionality. Information is stored in OLAP cubes and provides a multidimensional view of data.
  • Ad hoc analysis — BI tools that allow any user to make queries and generate a report to answer a specific question, often by using an OLAP “point and click” dashboard.
  • Reporting — BI tools that provide a visual representation of data that is extracted in a query such as charts, maps and graphs. Benefits of using BI reporting tools include increased speed, efficiency, and accuracy of reports used for analysis.
  • Advanced analytics — BI tools that are used by data scientists when constructing predictive and prescriptive analytical models. These autonomous or semi-autonomous tools have sophisticated capacities to predict future outcomes and make recommendations.
  • Operational BI — BI tools that process incoming data in real-time, giving visibility and faster access to information for decision-making. With real-time data and insights, a company can respond rapidly to market trends and events.
  • Open source BI — BI tools developed with open source code that can be modified as needed. These tools typically come as a suite of products with reporting and analysis capabilities included.
  • Self-service BI — BI tools that do not need any training in statistical analysis or data mining to use. Self-service systems are configured to allow any user to make queries, design reports, and gain insights using interactive dashboards.
How to Find the Best BI Tools

The first step in choosing a BI tool is to understand the type of data sources (schema and definitions) the organisation accesses, and how they will need to be analysed. Most data sources can be easily accessed by a BI tool, but there can be specific types of data that make it prohibitive. A cloud-native tool should be able to support data in various data repositories or data warehouses.


The next step is to define business goals and the desired outcome:


  • Identify KPIs you want the BI system to measure.
  • Assess costs and evaluate the technical skills necessary to manage the tool.
  • Decide if you need a standalone BI tool, open source BI tool, or BI suite of tools.

The right BI tool—or tools—should allow you to drill down to the finest detail and get precise answers filtered by source, time, and any other factor needed to fulfil a request. It should have ‘suggestive intelligence’ capabilities (automated with machine learning) that can find patterns in the data relevant to the question being asked and suggest solutions.


Other important features of a modern, robust BI tool include:


  • Can generate visual reports
  • Tracks progress and individual KPIs
  • Creates presentation-ready graphics
  • Has an easy-to-use, intuitive interface
  • Has robust security
  • Includes mobile applications
  • Automatically prioritises works tasks
  • Identifies problems early
  • Has a natural language interface
Business Intelligence Examples

More than just theory, when BI is implemented correctly it can transform an organisation. Here are a few examples:


1. Lenovo: The Power of Real-Time BI


Lenovo is the world’s largest PC vendor and a U.S. $46 billion personal technology company. They have built an elastic hybrid-cloud platform supporting real-time BI that annually analyses 11 billion+ transactions of structured and unstructured data.


Measured results of their new platform include: 18% increased attach rate for ThinkPad laptop series, 11% increase in revenue per retail unit with conjoint analysis, and $1 million reduced operating costs in six months.


2. McDonald’s: Data-Driven BI for Better Customer Service


McDonald’s used data-driven BI to improve customer service with a new approach to ETL, big data, and data quality.


Measured results include reduced need to buy new hardware and the ability to generate business-critical reports in a timely manner to forecast sales, ensure right staffing levels, and recruit new employees.


3. MoneySuperMarket: Aggregating Data for Improved BI


MoneySuperMarket (MSM) is the UK’s leading price comparison website. MSM leveraged the capacity of Amazon Web Services with Talend Data Management to get data from several web services into data warehouses to provide marketing BI.


Measured results include improved path to purchase for customers, channel performance forecasts enabled with daily metrics and data science applied to 11 terabytes of data supporting ad hoc analysis.


The Future of Business Intelligence: A.I. and the Cloud

The future of big data BI is the cloud and artificial intelligence. Pulling data from a production database and dumping it into a spreadsheet for BI reporting is becoming less common. As enterprises move to the cloud, the BI system is automated using cloud-native BI applications that extract insights, make suggestions, and create visual representations of the data.


Today the need to be data-driven and to address informational complexities and data modernisation, are the driving forces behind businesses cloud strategies. The top three reasons CIOs give for adopting cloud computing information technologies are to:


  • Improve agility and responsiveness.
  • Accelerate product development and innovation.
  • Save money.

Cloud computing offers new solutions for BI and big data management—with automated, cloud-native BI tools. It is estimated that by 2020, 40% of tasks performed by data scientists will be automated.


References:

http://www.scriptcaseblog.net/business-intelligence-vs-business-analytics/


https://www.talend.com/resources/what-is-business-intelligence/