Predictive Analytics Vs Business Intelligence: is There a Difference?

In the modern world, technology used in everyday business processes can confuse non-technical people. Many technologies may seem to do the same job, but in reality have very different functions depending on the way they are used. One example of this is the confusion between business intelligence and predictive analytics. The two processes overlap in some instances, making it difficult for those without a tech background to understand exactly what is going on.

Business intelligence and predictive analytics both use sophisticated mathematics to analyse data in order to solve business problems. When people talk of business intelligence or data mining, they usually associate the concept with an analytics tool-kit that automatically searches for any useful patterns in large data sets from Big Data. Predictive analytics on the other hand is an analyst-guided discipline that takes advantage of multiple data patterns to make future forecasts or to make complex statements about your target niche.

Business intelligence searches for clues and analyses the past while predictive analytics delivers answers to guide your business on what to do next. Business intelligence is an important stage when developing a predictive model. Automatic data mining techniques have the ability to isolate value data types within a vast field of data possibilities. The data analyst uses variables and patterns to build a mathematical model that will formalise the relationships and predict future behaviour.

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What is Business Intelligence?

Let’s expand our understanding of business intelligence. Traditional business intelligence or BI focuses on retrospective analysis, which means reporting on what has happened and what is currently happening. Business intelligence has always been an essential component in effective decision-making and it’s now enhanced with the help of predictive analytics.

Business intelligence is defined as a concept that is made up of techniques, information and tools used in transforming raw data into meaningful, useful and effective information for the purpose of business analysis. The process involves different software applications and several related activities including data mining, online analytical processing, querying and reporting. The technologies used in business intelligence are capable of handling very large amounts of unstructured information in order to identify, create and develop new strategies and other business opportunities. It also allows any organisation taking advantage of its benefits to develop calculated decisions based on what will be right for the organisation. By effectively using business intelligence, managers will be able to formulate effective strategies so their businesses can effectively compete in the market. Business intelligence tools are more focused on exploration than action-oriented processes. Business intelligence also helps organisations understand business performance and market trends.

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10 Benefits of Business Intelligence

1. Get Answers Quickly

If you have implemented a business intelligence solution, you will not need to spend too much time sorting through mountains of data and printed reports. BI solutions will bring you quick answers to the most pressing business queries.

2. Lowered Staff Expenses

One of the most tangible advantages of business intelligence is the automated processes. Data collection, aggregation and report generation will be automated and so staff won’t need to spend hours on these tasks.

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3. It Aligns The Business To Its Objectives

In all the an all-encompassing view of the organisation’s data, upper management and executives can ensure that business processes are aligned with the proper KPIs as well as both long term and short term goals.

4. Easily Acquire Actionable Data

Business intelligence solutions present a consolidated view of the information you want with just a few clicks instead of manually sorting through data. This offers actionable data to anyone who needs it. Business intelligence solutions results in new insights by providing ad hoc query capabilities that allow users to find what they are looking for more efficiently.

5. Removes Bottlenecks

If an organisation does not implement a BI solution, there will be bottlenecks in processes. Once a business intelligence solution is implemented, users will have the ability to generate reports without going through the busy IT queue.

6. Get Reports Faster

Business intelligence solutions provide companies with the ability to analyse data quickly, which makes report generating a much faster process. Plus many analysts will be able to save time and focus on their core functions and other pressing issues.

7. Save Capital and Other Expenses

Upper management and supervisors can focus on other processes that require more attention. Most manual processes will be replaced by automation, saving the business money which can then be used for other purposes.

8. Key Metrics Whenever You Need Them

Having quick access to quality information by way of business intelligence will allow upper management, executives and managers to view and assess key business processes and metrics at any time.

9. Track Your Progress

Business intelligence solutions will highlight data which allows you to track your KPIs and compare them with past and present performance.

10. Planning For The Future

Having access to past and present information will allow your business to properly plan for the future. Business intelligence can help your business get a glimpse of the future by recognising the possible opportunities and trends, increasing your chances of getting ahead of the competition.

Possible Disadvantages and Risks of Business Intelligence

Automation, predictions and overall faster work sounds like a great deal, however, business intelligence may have some disadvantages when not implemented or used correctly.

1. Your Business Intelligence Solution isn’t Good Enough

Project managers can be easily duped by smooth talking BI sales people. Before entering any contracts, make sure a solution fits your needs, your data types, the users, the reporting needs and the overall business goals. Ask questions and evaluate different vendors and service providers before making a decision.

2. Not Flexible to Change and Other Advancements

Many business intelligence implementations are not flexible to advancements and some people will be resistant within the designated user groups. This usually happens when the functions, useability and the value of the project has not been clearly communicated to the users.

3. Failing to Account for Change

Once a new business intelligence solution has been implemented, the culture, focus and environment of the organisation will change. The BI requirements, project parameters, reporting needs, data models and data resources  will always be in a state of relative flux. If the organisation fails to make necessary changes it will lead to the failure of the BI solution.

4. Poor Data Quality

If you bypass an adequate clean-up of your data and do not implement strict data change management policies before rolling out a BI solution, it may result in disaster. Any meaningless and inaccurate reports will surely damage the perception of the business intelligence project. In this case, first impressions are really important.

5. User Adoption Is Low

This is the business intelligence team and project manager’s worst nightmare. No matter how good your business intelligence strategy is, if no one wants to use it, it will be a waste of time and resources. The potential benefits and the return on investments of the organisation will never be realised.

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What is Predictive Analytics?

Predictive analytics are defined as the use of data, machine learning techniques and statistical algorithm techniques in identifying the likelihood of future outcomes based on historical data. It can also be defined as a branch of data mining focused on collecting and analysing past and current data to make calculated predictions, probabilities and future trends. Predictive analytics automatically analyses large amounts of data using different variables and clustering, neural nets, text timing, regression remodelling and hypothesis testing. It combines solid business knowledge and statistical analytical techniques to make calculated predictions. The main purpose of PA is to explicitly direct individual decisions. With predictive analytics, your business can leverage all available data resources and discover patterns to unlock the intelligence to help you take the appropriate actions in achieving your key business goals.

Predictive analytics can help your business  understand how your organisation is performing and enable you to define rules for making better business decisions based on the predicted outcomes. From discovering customer pain points and operational risks to developing highly nuanced market segments for targeted marketing, predictive analytics will take your business intelligence to the next level.

Types Of Predictive Analytics

There are several types of predictive analytics that are in use today in different enterprises and applications. These include:

1. Predictive Modeling

This type of predictive analytics involves the use of mathematical modelling associations with variables in historical data to predict or to forecast a future event or likelihood. This type of analytics is mostly used in the financial industry to predicting the behaviour of capital markets, revenue forecasting sales, online recommendation systems, dynamic pricing, strategic planning and other business processes that require future decision-making.

2. Decision Analysis and Optimisation

This type of predictive analytics focuses on reducing the uncertainty inherent in decision-making. It involves analysing decision trends to find which is most likely to give the best results. This type is mostly applied in the supply chain management industry to maximise revenue and achieve key performance goals.

3. Transaction Profiling

This type of analytics focuses on aggregating and filtering information from transactions involving enterprise software. It can include – but is not limited to – logins to social networks, a retailer’s website, credit card transactions and isolated data points. The process involves standardising this data and clustering it with relevant data that will allow organisations to develop predictive models of transactional data.

4. Predictive Search

This involves developing algorithms that take one set of input and find a particular output. With the increasing sophistication of inputs, algorithms need to be optimised to give the best possible results.

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Predictive Analytics Applications

Some common functions and applications where predictive analytics has proven useful include:

  1. Marketing. Many modern organisations use predictive analytics in determining customer response or purchases and to promote cross-selling opportunities. Predictive models help businesses attract, convince, retain and grow the most profitable customers and maximise their marketing spending. Apart from identifying prospects, predictive analytics can also help identify the best combination of product versions, communication channels, marketing material and timing to be used in targeting a specific market or customer type.
  2. Security and fraud detection. Predictive analytics has the ability to stop losses due to fraudulent activities before they happen. By mixing multiple detection methods, business rules, link analysis and anomaly detection, a business can get enhanced accuracy and better predictive performance. There are high-performance, behavioural analytics that check all actions on a network in real-time by spotting abnormalities that can indicate occupational fraud and advanced persistent threats.
  3. Credit scores. Credit scoring is one of the most well-known applications of predictive analytics. Credit scores are used to assess a buyer’s likelihood of default purchasing. A person’s credit score is a figure generated by a predictive model that combines all the data relevant to credit capability. Other risks-related uses of predictive analytics include insurance claims and collections.
  4. Clinical decision support system. The use of predictive analytics in health care is to determine which patients are at a higher risk of developing certain conditions like asthma, heart disease, diabetes and other chronic diseases. Sophisticated clinical decision support systems also take advantage of predictive analytics.
  5. Analytical customer relationship management. This is one of the most frequent commercial uses of predictive analytics. This applies to customer data in pursuing CRM objectives which involves developing a holistic view of the customer no matter where the information is stored in the company or department.
  6. Manufacturing. Predictive analytics is used in this industry to identify factors that lead to reduced quality and production failures and optimise distribution and service resources. Computer manufacturer Lenovo used predictive analytics to better understanding warranty claims that lead to a 15% reduction in warranty costs.
  7. Entertainment and media. Media and entertainment companies can get deep insights into audience preferences by knowing the influencing attributes, trends, drivers and the desires across properties. It also scores the visitors to determine appropriate audience segments and behaviour value.
  8. Retailing. Retailers will be able to assess the effectiveness of their promotional events and campaigns, plus predict which of their offers are the most effective for customers, determine which products to stock and how and where to build brand and customer loyalty.

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Limitations of Predictive Analytics

Everything has its limitations, including a good predictive analytics solution. Some limitations you may encounter include:

  1. Incomplete Data. Any missing values will surely limit the functionality and usability of your predictive analytics strategy. You have to make sure that you’re looking at a time frame that gives you a complete scenario of the fluctuations of your data.
  2. People do not always provide accurate information. If you’re collecting data from surveys, expect that some people will not provide accurate information. Some questions may be embarrassing or they just simply don’t want to answer the questions truthfully.
  3. Data format. Data collected from different sources will differ in format and quality. Surveys, emails and data entry forms will have different structures and attributes. Compatibility will be an issue among the different data fields and sources. To correct this, you will need to do some major pre-processing.

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As you can see, both business intelligence and predictive analytics make up a large part of business processes. Analysing the past with these two solutions will help forecast the future and increase your chances of reaching high priority business goals.



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