The power of business analytics in achieving organisational success
What is business analytics?
Industries today generate a staggering amount of information and data, which has resulted in an ever-growing need for organisations to readily interpret and analyse information. With this key business need comes business analytics: a powerful tool at the disposal of businesses in today’s marketplace.
Businesses and organisations across the world today require these analysis tools to interpret and analyse large amounts of information, with many considering data to be the ‘new oil’.
Business analytics is the methodical process of exploring quantitative methods to derive meaning from data. It’s these analyses that give you the ability to make informed business decisions based on valuable insight.
By analysing historical data, organisations are able to determine industry trends and patterns to create what is called predictive analytics, or the process of using statistics to forecast future outcomes.
Four primary methods
Business analysis can be categorised into four primary methods, which we’ll explore in more depth further on. These four types of analytics are: descriptive, diagnostic, predictive, and prescriptive.
Descriptive business analysis is the interpretation of historical data in order to recognise patterns and trends.
Diagnostic business analytics puts a focus on past performance in an effort to understand what happened and why.
Predictive business analysis is the use of available information and statistics to predict future outcomes.
Finally, prescriptive business analysis is the application of tests and other techniques to determine what method will offer an organisation the best result under certain situational circumstances.
Business analytics vs. business intelligence
Business analytics is often used synonymously with business intelligence, although there are some distinct differences between the two.
Oftentimes, more advanced areas of business analytics begin to resemble data science, but this doesn’t necessarily mean that data science is involved — regardless of whether advanced statistical algorithms are being applied to data sets or not.
A key distinction between data scientists and business analysts is that whilst business analysts often aim to solve a specific question, data scientists typically don’t.
Instead, data scientists endeavour to allow data to guide their analysis as they explore this information using advanced statistical methods. With the advanced business analytics tools available today, many of them perform these kinds of functions automatically.
Conversely, business analytics works on the basis of a determined business goal. Once determined, a method of analysis is chosen and data is then acquired to support the analysis.
First, analysis is typically performed against a smaller sample size, and then as patterns in the data begin to emerge, new questions are formed, and the analysis persists until the business goal is met.
Benefits of business analytics
With the amount of information being created today, business analytics presents a wealth of benefits to organisations and industries, all of which need valuable insight to make meaningful business decisions.
Knowledge is power, and in the modern world, the amount of data available to us can provide a wealth of new and exciting information.
With the acquired ability to make more informed decisions, businesses can benefit from greater financial returns. As they begin to implement business analytics into their decision making processes, organisations are able to extract more useful information from the data available to them.
This can prove catalytic to revenue growth, and can subsequently offer the confidence boost needed to make more effective and efficient decisions.
The benefits to you, however, don’t begin and end at financial gains. Business analytics offers you the opportunity to extract useful predictive analytics from historical data. With improved insight from this information, businesses can also begin to improve a lot of internal practices by fine-tuning operations.
New data and business operations also creates new opportunities for problem solving within an organisation. Business analytics can be a precious resource when approaching certain strategic decisions. With more valuable information, faster reporting, and improved analytics, there soon follows a growth in actionable insights within an organisation.
Types of business analytics
The complexity and size of big data is beyond human comprehension, which is why the first stage of business analytics needs to condense this data into intelligible pieces.
Earlier, we briefly mentioned the four types of business analytics – descriptive, diagnostic, predictive, and prescriptive – and what they mean. Now we’ll take a deeper dive into each of the respective forms.
Descriptive analytics
Once again, descriptive analytics is a summation of existing data to better understand what is, or has, happened in order to recognise patterns or trends. This is often considered to be the simplest form of business analytics, as it’s used simply to build a foundation upon which much more analysis can take place.
It’s believed that 80% of business analytics primarily involves descriptions based on compilations of information from past performances. It’s important for businesses to make raw data understandable, as this can be offered to investors, shareholders, and managers to address business strengths and weaknesses.
Diagnostic analytics
Unlike descriptive analysis which focuses on ‘what’ happened, diagnostic analytics takes a deeper look at why something happened in the past. This is helpful in understanding and pinpointing what factors or events contributed to specific outcomes.
This type of analytics, however, has a limited ability to provide actionable insights as it primarily focuses on providing an understanding of causal relationships while looking backward.
Predictive analytics
Predictive analytics, as mentioned, is the use of statistics to predict future outcomes. It’s important to understand that this form of analysis does not predict what will happen in the future, but rather it forecasts what the probabilities of the occurrence of certain events are.
The essence of predictive analytics is to create models to help understand existing data, and to predict future data or occurrences. This form of analysis relies on existing data to create future predictions, as these predictions cannot be obtained otherwise.
Prescriptive analytics
Prescriptive analytics goes beyond the aforementioned forms of analytics to suggest future solutions based on previous findings — particularly those determined with predictive analytics.
Prescriptive analytics can suggest all favourable outcomes based on a specified course of action, and can also suggest alternative actions to reach a particular result.
Often you may find that computations include optimisation of functions related to specific results, although another approach involves a simulation in which key performance areas are combined to design the right outcome.
This alternative approach ensures that key performance metrics are included in the desired outcome.
Challenges of business analytics
With the vast amount of data produced every minute, businesses face new and exciting challenges that were once thought to be inconceivable. Storing, managing, using, and analysing such large amounts of data prove difficult to even the largest of businesses, all of which strive to make this data useful in some capacity.
It’s not surprising that data is growing and changing every day, but it means that businesses are being forced to find ways to incorporate them into new or existing analytical platforms.
Overlooked data can cause big problems in the way organisations understand the information, and the insights drawn from incomplete data sets can result in inaccurate predictive analytics. Given the amount and variety of data that is accessible today, it is vital that data is made easily accessible for businesses to store and use.
Beyond the obvious challenge of overlooked data, businesses and industries today are also faced with a shortage of professionals who understand big data analysis. Having the data is one thing, but simply storing this information is not going to be useful to you.
With such huge data sets, and an acute shortage in people who understand it, businesses are also faced with the challenge of extracting meaningful insights from big data analytics.
With the rapid growth of businesses, there too comes an increase of data being produced. As the amount of data increases, however, the storage of this data becomes a growing problem.
Whilst there are options such as data lakes and warehouses for companies to gather and store large amounts of unstructured and structured data, the problem arises when an attempt to combine this data causes errors. Missing or inconsistent data, conflicts in logic, and data duplication all result in quality challenges.
And beyond challenges of simply storing the procured data, there are huge risks associated with big data when it comes to privacy and security. Data storage and analysis leads to a high risk of exposure and vulnerability, and the rise of voluminous amounts of data increases many of these concerns.
Tools of business analytics
Business analytics is a powerful tool, and there are a variety of options that businesses can choose from to harness this power.
Power BI is a robust data visualisation platform created to empower businesses in their journey to success by making them more efficient and effective throughout.
With a complete view of your business on a single dashboard, Power BI allows anyone in your team to analyse and visualise insightful data across any device wherever they are, helping to increase revenue, decrease costs, and encourage growth.
As a Microsoft tool, it also integrates seamlessly with Microsoft Teams, Office 365, SharePoint, Dynamics, Adobe Analytics, Salesforce, Google Analytics, and many more.
Another platform to consider is Microsoft Azure. The end-to-end machine learning of this platform offers developers and data scientists a range of opportunities for training, building, and deploying machine learning models more efficiently.
Microsoft Azure Machine Learning offers productivity for all skill levels with drag-and-drop designers and automated and responsible machine learning.
Businesses can also bring their data to life with Cognos Analytics, which uses artificial intelligence to unleash your businesses potential.
Now more than ever, Cognos allows you and your team to visualise business performance with its powerful and customisable dashboard and reporting capabilities.
With IBM Cognos Analytics, organisations can gain faster insight to make meaningful decisions with dynamic and intuitive data analytics, and choose who has access to sensitive information within the team.
At Simpson Associates, we pride ourselves in our expertise in cloud migration strategy and can help your organisation understand how the Cloud can be the best choice to meet your business requirements.
From there we can demonstrate how Microsoft Azure, Power BI, or IBM Cognos Analytics can benefit your business and enable you to quickly realise return on investment. Our expert team can guide you through the process, ensuring that we use the best technology and solution to suit your organisation.
FAQs
What is business analytics in simple terms?
Business analytics is the process of exploring quantitative methods to derive meaning from data. Business analysis can be categorised into four primary methods: descriptive, diagnostic, predictive, and prescriptive.
What is the difference between business analytics and business intelligence?
With business intelligence, data scientists typically don’t aim to solve a specific question, and instead endeavour to allow data to guide their analysis as they explore this information using advanced statistical methods. Business analytics, however, works on the basis of a determined goal, after which analysis persists until business goals are met.
What are the benefits of business analytics?
With business analytics, companies can make more informed decisions, which can offer greater financial returns. Much of the decision making that stems from business analytics can help improve business operations and help businesses solve problems, through which they can find actionable insights from acquired data.
What are the tools used in business analytics?
Microsoft Power BI, Microsoft Azure Machine Learning, and IBM Cognos Analytics are all robust data visualisation platforms that offer businesses a platform on which they can acquire meaningful insight for better business decisions. Each platform should be understood in depth to determine which tool is most suitable for your business needs.
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If you have any questions about business analytics, business intelligence, or any of the tools mentioned in this article, please feel free to speak to us using our live chat, where one of our experts will be happy to speak with you in more detail.
Blog Author
Andrew Edge, Data and AI Solution Lead, Simpson Associates
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