Analytics is something every business needs to stay competitive in today’s datafilled world. Every manager needs to at least understand the basics of analytics and when and where to apply it. It is impossible to open a leadership or management journal without reading something on the explosion of ‘big data’, ‘analytics’, ‘business intelligence (BI)’, knowledge management’, ‘data mining’, ‘data discovery’ or ‘decision support’.There is often a great deal of confusion around these terms and often they are used synonymously and interchangeably, which can often amplify the confusion. There is a great deal of interest in this area because it promises to unlock commercially relevant insights that can potentially be used to uncover new markets, new niche audiences within markets and areas for future research and development.

Highly publicised stories and business case studies from data gods like Target, Walmart, Amazon, Facebook and Google can leave normal business leaders feeling vulnerable and overwhelmed – unsure of where to start or what to do in order to ‘catch up’. The simple fact is that for most businesses it’s impossible to reach those lofty data analytic heights, but that doesn’t mean analytics is only for the big guns. Nothing could be further from the truth. Analytics can improve performance in every business regardless of size but in order for it to deliver its promise we first need to understand it and dispel some of the fear around it – and that’s where this book comes in. In essence, analytics is about data and how we can use it to improve business success and performance.

Clearly this concept is not new, business leaders and senior executives have been using past performance and business data for decades to help decide strategy and alter course when necessary. But what is new is our ever-expanding definition of what data is and the technological advances that allow us to store, analyse and extract value from data that was previously impossible. The raw material – data The raw material of this insight extraction process is data – whether that is traditional data or ‘big data’. Currently the term ‘big data’ is used to describe the fact that everything we do, say, write, visit or buy leaves a digital trace, or it soon will, and the resulting data can then be used by us and others to gain new insights and improve results.

Although the term ‘big data’ will probably disappear as ‘big data’ becomes plain old data, it is currently considered ‘Big’ because of 4 Vs: ●● Volume – relating to the vast amounts of data that are being generated every second not least because of our love affair with smart technology and constant connectivity. ●● Velocity – relating to the speed at which new data is generated and moves around the world. For example, fraud detection analytics tracks millions of credit card transactions for unusual patterns in almost real time. ●● Variety – relating to the increasingly different types of data that are being generated from financial data to social media feeds, to photos to sensor data, to video footage to voice recordings. ●● Veracity – relating to the messiness of the data being generated – just think of Twitter posts with hash tags, abbreviations, typos, text language and colloquial speech. Used effectively the 4 Vs can also deliver the 5th V – Value. And that’s what analytics is really all about – the use of data to deliver value. And analytics allows us to derive value by answering four key questions: 1 W hat happened? 2 W hy did it happen? 3 W hat’s happening now? 4 W hat might happen in the future? Clearly these are important questions to know the answers to and analytics makes it possible.

The easiest way to think about business analytics is that it is the process by which you take the raw material (data) and convert it into commercially relevant insights (analytics) that can inform business, improve performance and guide strategy (business intelligence). Of course the validity and accuracy of that process depends on how clear you are about the key strategic questions you are seeking to answer and the quality of the data you use to answer those questions. So before we dive into the various key analytics let’s step back and get really clear about the types and formats of data that can now be analysed. Data types and format When it comes to data there are a few key distinctions that are important to understand.

Data is either structured, semi-structured or unstructured, and it is sourced from either inside your business or outside your business. Structured data is data that is highly organised and located in a fixed field within a defined record or file. This includes data contained in relational databases or spreadsheets. Structured data is easy to input, easy to store and easy to analyse because it follows rules and is often accessed using Structured Query Language (SQL). While SQL represented a huge improvement over paper-based data storage and analysis not everything in business fits neatly into a predefined field and that’s where semi-structured and unstructured data comes in. It is estimated that 80 per cent of business-relevant information originates in unstructured or semi-structured data.

And essentially it’s everything else that can’t be easily inserted into fields, rows or columns. It is often text heavy but may also contain dates, numbers and different types of data such as images or audio files. Semi-structured data is a hybrid of unstructured and structured. This is data that may have some structure that can be used for analysis but large chunks are unstructured. For instance, a LinkedIn post can be categorised by author, date or length but the content is generally unstructured. Likewise, word processing software includes metadata detailing the author’s name, when it was created and amended but the content of the document is still unstructured. Of course, the source of data is also important because most businesses are already data rich.

The problem is they are insight poor and don’t often know how to use the data they have, never mind utilise the treasure trove of external data that also exists. As a rule of thumb, internal data is usually easier and cheaper to access because it is owned and controlled by the business. This might include financial records, customer feedback, transaction history, employee surveys, HR data, etc. External data as the name would suggest is any data that exists outside your business which is held either publicly or privately by another organisation. If data is public then you can collect it for free, pay a third party for it or hire a third party to collect it for you. Private data is usually something you would need to source and pay for from another business or third party data supplier. External data might include weather data, social media profile data, trend data or government-held data such as census information.