Applying big data and data analytics in Strategic Business Leader
The article will first give an overview of big data and what it is and its characteristics. It will then explain data analytics and what types of analytics can be of most use to business leaders. The article will also look at some examples of how using big data and data analytics can improve business performance, focusing on aspects such as being sceptical about the use of data and most importantly how important it is to use data ethically, responsibly, and securely to minimise reputational and financial risk.
This article is also written to explain what an SBL candidate would need to know to apply these outcomes in a potential examination question. The above outcomes essentially cover three themes. The first is how big data and data analytics can inform and implement strategy; the second is how big data and data analytics can provide new opportunities and present new risks for businesses and the third is how big data and data analytics helps business leaders make better and more effective business decisions in a variety of ways, in order to create and sustain business value.
What is big data?
Big data is about much larger, more complex data sets in all forms being available to businesses, from structured and semi structured to completely unstructured data. It comes from a variety of new and existing sources and these are increasing as more and more people carry out most of their activities on electronic devices where the data is recorded. Data therefore has a great deal of potential value.
Data, which is not used or analysed has no value, but value can be added to data if it is cleaned, processed, transformed and analysed. Therefore data collected can be considered to be the raw material as in a manufacturing process. Some of this raw material is unrefined and some refined, such as structured and unstructured data. This data needs to be stored in a virtual warehouse such as the cloud. The cleaning and transformation of the data into a suitable form for analysis is really where the value is being added, so that the data can become the finished product – which is useful information which needs to be delivered or communicated to the user. Reliable, timely and relevant information is what the business leader needs in order to answer key business questions and make more informed business decisions.
Today, big data has become an important form of organisational capital. For some of the world’s biggest tech companies such as Facebook, a large part of the value they offer comes from their data, which they are constantly analysing to produce and develop new revenue streams.
Sophisticated data analytics is about accessing the data which is useful for decision making and the five characteristics that big data has to improve the quality of decision making are:
These are known as the five Vs of big data.
The amount of data matters greatly and the more that can be accessed the better. Most of this may have no value, but no one will know until they try and structure it and use it. This data can come from a wide range of sources, but could include social media data, hits on a website, or results from surveys or approval ratings given by consumers. For some organisations the sheer amount of this data will be difficult to manage unless the organisation has the right capabilities, including adequate storage and processing capacity. The main thing about the volume of big data is the additional reliability it gives the data analyst. As any statistician knows, the more data you have, the more reliable your analysis becomes and the more confident you can be about using the results you obtain to inform decision-making
Velocity is the rate at which data is received and used. In the modern world, transactions are conducted and recorded in real time. As people increasingly shop with debit and credit cards and use their phone apps, these transactions are updated immediately.
Stores themselves know exactly how much inventory they have and the sales they are generating on a transaction by transaction basis and because customers transact with them electronically, they also know a lot more about their customers and their buying patterns. The banks too, immediately know that funds have gone out of customers‘, and into their suppliers’ accounts in real time.
Variety refers to the many types and sources of data which are available. Traditional data types were more structured. With the rise of big data, data comes in new unstructured types. This also comes in a variety of forms. These include numerical data, text, audio, pictures and videos. All these require additional processing to transform them into meaningful and useful information which can be used to support decision-making, but being able to access them and use them provides richer information for the business leader which can make the information obtained from the data analysis more relevant and significant than larger amounts of data from more structured sources.
The three Vs discussed above are all clear benefits of big data. However, the data analyst must be sure that the data they are using to perform data analytics has veracity. Veracity means reliability and robustness and it is no good having large amounts of data in real time from a wide range of sources unless it can be relied upon.
Finding value in big data isn’t only about analysing it, it is a process of discovery which requires insightful analysts to recognize patterns and trends, and for business leaders to ask the right questions, to make informed assumptions, to accurately predict behaviour and come up with relevant solutions. If for any reason the original data is somehow unreliable, assumptions can be wrong, or the interpretation of the original business question or issue might be incorrect. In these circumstances the data analysis may yield incorrect or irrelevant information. It is therefore important that the data analyst or business leader, using the findings of data analysis, is sufficiently sceptical of the information that comes out of the data analytics process and properly challenges or verifies the information received.
Data analytics techniques
There are unlimited ways in which data can be analysed, depending on the number of business questions which can be asked by business leaders about the businesses which they lead. Essentially the data analytics process should always begin with posing a specific business question such as the following:
- Are we losing money or inventory due to fraud and error and if so where and how much exactly are we losing and why?
- Which products or customers are the most profitable and why?
- How can I predict my sales if the weather or other factors change?
- What is the cheapest way of distributing our goods from the warehouses to the stores?
- At what price should I sell my new product to maximise my profit?
There are many more of these questions and the SBL examination could address such questions and require the candidate to assess how such problems could be resolved and the solutions arrived at could involve some application of their knowledge about data analytics.
Essentially there are three types of data analytics:
Descriptive analytics is the analysis of data to observe what has been and is currently happening. Examples of this could be to analyse data by product, by outlet and by customer. Such analytics would allow the business leader to observe past trends, to analyse and classify data in different ways to draw conclusions about the data which might be relevant to informing strategy or to support or make more effective decisions, such as minimising risks or exploiting opportunities.
Often descriptive analytics is enhanced by the use of graphs or charts as commonly available in spreadsheet packages and pivot tables are particularly useful for presenting information in different ways. Pivot tables are a powerful spreadsheet tool which allows the business analyst to reclassify, filter and present information in a variety of ways so that better visualisation of the data and insights into it can be achieved.
An example of this is where retail data on the sales, cost of sales (COS) and gross profit margin (GP) in six retail outlets of a range of five products within each store are tracked over time to establish trends.
By looking at the overall figures for the company as a whole, or even by individual product across the company or for a store as a whole, the business leader may not notice any unusual trends or departures from the expected levels, from a chart or graph of these measures. See how all these metrics are reasonably constant.
Only by analysing and charting these trends more closely by product in each individual store (such as by using pivot tables) could the business leader detect if and where there is any specific fraud or loss and such discrepancies would become more apparent if this type of micro level descriptive analysis is undertaken. In the above example it looks like there was a problem with Product 2 in Store 6 – see here.
In the above example when the trend for Product 2 in Store 6 is examined more closely, it can be seen that the GP margin falls from 33% down to about 17% and it is nothing to do with sales which remain constant, but is caused by a significant change in COS which rises from just above $800 in periods 1 and 2 to $1000 by period 5. In this case the business leader would be looking at a potential loss or theft of inventory relating to this product and would need to investigate further.
Predictive analytics involves using techniques to allow the business leader to evaluate strategies and to anticipate strategic outcomes depending on which scenarios play out. ‘Scenario Manager’ in Excel is an excellent tool for doing this and business leaders. Another spreadsheet tool is the Data Analysis Pack within Excel which allows business analysts to carry out multiple linear and nonlinear regression and a range of other statistical techniques on data. This allows the analyst to test the relationship between independent or input variables with a dependent or output variable. An example of this is where weather conditions such as hours of sunshine, amount of rain and temperature levels can affect sales of certain items, such as barbecues or cold drinks. Knowing how well these factors are associated with the dependent variable in the past, such as the sales of certain products, allows business leaders to predict future sales more accurately when the independent variables are established.
However, when using such techniques, a degree of scepticism must be applied because sometimes whilst some independent variables are good predictors of the dependent variable, they may not be the cause. Take the price of petrol as the independent variable and the sales of motorcycle helmets as the dependent variable. The price of petrol is a good predictor of the sales of motorcycle helmets as there is a very high positive correlation between the two, but one is not the direct cause of the other. This is because as the price of petrol rises, the sales of motorcycles increase because they use less fuel. But because in most countries the law requires the motorcyclist to wear a helmet for safety reasons, the outcome is that the sales of motorcycle helmets, being a complementary product, also increases.
Prescriptive analytics is potentially the most powerful form of data analytics for decision makers and business leaders and is the field of analytics which is concerned with optimisation, such as maximising sales, minimising costs or maximising profit. Prescriptive analytics draws upon techniques which are commonly available in spreadsheets such as ‘Goal Seek’ and in particular ‘Solver’.
A good business example of using solver as a data analytics tool is shown below with a problem to minimise transportation costs:
A company wishes to minimise its costs of delivering televisions from three depots (D1, D2 and D3) to three stores (Store 1, Store 2, and Store 3).
The problem is set out on an Excel spreadsheet – see here.
In the above example, the cost per mile of delivering TVs; the distances between depots and the stores and the capacities of the stores to hold TVs are given.
From the solver parameters table displayed, it can be seen that the Solver objective is to minimise the total cost in the yellow cell E22, subject to the constraints that the total allocations of TVs to each store from all depots cannot exceed the maximum capacity of the store to hold TVs and that the total number of TVs transported from the depots cannot exceed the number of TVs held at each depot. The optimal solution is presented in the green figures shown in the range C15:E18 which the model changes to find the optimal or best solution.
Applications data analytics in future SBL exams
In SBL, students would not be expected to carry out such analysis as has been shown in this article, but these have been discussed to give students some insight into the type of analytics which can be carried out. This would normally be done by expert data analysts on the behalf of someone in the role of a business leader.
Students studying for SBL only need to be aware of how big data and such techniques of data analytics can be of use in leading or managing a business more effectively. Candidates need to be aware that using data effectively is critical to a successful business and a key leadership trait is being able to interpret findings prepared by experts in data analytics in a sceptical, but insightful way, in order to make sound decisions.
Referring to the data analytics outcomes in SBL set out at the beginning of this article, it is possible to anticipate how big data and data analytics might be examined in a wider leadership examination by the examining team.
First of all, knowledge of big data characteristics and the wider benefits of big data and the ability of new data technologies to collect, store and process such data is an important business capability.
A business needs to have strategies to develop these capabilities to become successful and to compete more effectively.
An SBL examination might include a business which is losing or gaining competitive advantage because of its data analytics capabilities and require the candidate to identify these issues and consider ways to improve or exploit these capabilities.
An SBL examination might look at a company facing threats from disruptive technologies which need to be safeguarded against. Such threats might be from competitors like ‘Rent a Room’ which was featured in the December 2018 exam, but could also include security threats from people using technologies to steal data from companies or the risk of companies using data irresponsibly themselves and being threatened with litigation from the owners of such personal data, such as in the Facebook situation, and determining how this can be prevented.
Other scenarios could include situations involving business leaders using the findings of data analytics inappropriately or failing to exercise sufficient scepticism about the information they obtain from the data being analysed, or using such information unethically.
Finally, an SBL examination might include exhibits related to data analytics such as a spreadsheet, charts or multiple regression outputs and ask students to interpret or apply the findings, exercising scepticism to establish to what extent such information can be relied upon. These exhibits might include considering the best way to utilise scarce resources, to minimise costs in producing a range of products or how to optimise pricing or sales volume and the implications of doing this.
SBL candidates must be aware that big data and data analytics is a fast growing and emerging area which will apply across many business functions including, accountancy, audit, performance management, sales, marketing production and procurement. It is therefore very important that candidates read widely about these developments, but more importantly understand how such developments are relevant to improving business leadership.
Written by a member of the Strategic Business Leader examining team