Analytics is something every business needs to stay competitive in today\u2019s datafilled world. Every manager needs to at least understand the basics of analytics and when and where to apply it.\u00a0It is impossible to open a leadership or management journal without reading\u00a0something on the explosion of \u2018big data\u2019, \u2018analytics\u2019, \u2018business intelligence (BI)\u2019, knowledge management\u2019, \u2018data mining\u2019, \u2018data discovery\u2019 or \u2018decision support\u2019.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\u00a0relevant insights that can potentially be used to uncover new markets, new\u00a0niche audiences within markets and areas for future research and development.<\/p>\n
Highly publicised stories and business case studies from data gods like Target,\u00a0Walmart, Amazon, Facebook and Google can leave normal business leaders feeling\u00a0vulnerable and overwhelmed \u2013 unsure of where to start or what to do in order to\u00a0\u2018catch up\u2019. The simple fact is that for most businesses it\u2019s impossible to reach those\u00a0lofty data analytic heights, but that doesn\u2019t mean analytics is only for the big guns.\u00a0Nothing could be further from the truth. Analytics can improve performance in every\u00a0business regardless of size but in order for it to deliver its promise we first need to understand\u00a0it and dispel some of the fear around it \u2013 and that\u2019s where this book comes in. In essence, analytics is about data and how we can use it to improve business\u00a0success and performance.<\/p>\n
Clearly this concept is not new, business leaders and\u00a0senior executives have been using past performance and business data for decades\u00a0to help decide strategy and alter course when necessary. But what is new is our\u00a0ever-expanding definition of what data is and the technological advances that allow\u00a0us to store, analyse and extract value from data that was previously impossible.\u00a0The raw material \u2013 data\u00a0The raw material of this insight extraction process is data \u2013 whether that is traditional\u00a0data or \u2018big data\u2019. Currently the term \u2018big data\u2019 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\u00a0results.<\/p>\n
Although the term \u2018big data\u2019 will probably disappear as \u2018big data\u2019 becomes\u00a0plain old data, it is currently considered \u2018Big\u2019 because of 4 Vs: \u25cf\u25cf Volume \u2013 relating to the vast amounts of data that are being generated\u00a0every second not least because of our love affair with smart technology and constant connectivity. \u25cf\u25cf Velocity \u2013 relating to the speed at which new data is generated and moves\u00a0around the world. For example, fraud detection analytics tracks millions of credit card transactions for unusual patterns in almost real time. \u25cf\u25cf Variety \u2013 relating to the increasingly different types of data that are being\u00a0generated from financial data to social media feeds, to photos to sensor data, to video footage to voice recordings. \u25cf\u25cf Veracity \u2013 relating to the messiness of the data being generated \u2013 just think\u00a0of Twitter posts with hash tags, abbreviations, typos, text language and colloquial speech. Used effectively the 4 Vs can also deliver the 5th V \u2013 Value. And that\u2019s what analytics is really all about \u2013 the use of data to deliver value. And analytics allows us to\u00a0derive value by answering four key questions: 1 W hat happened? 2 W hy did it happen? 3 W hat\u2019s happening now? 4 W hat might happen in the future? Clearly these are important questions to know the answers to and analytics makes\u00a0it possible.<\/p>\n
The easiest way to think about business analytics is that it is the process\u00a0by which you take the raw material (data) and convert it into commercially relevant\u00a0insights (analytics) that can inform business, improve performance and guide\u00a0strategy (business intelligence). Of course the validity and accuracy of that process depends on how clear you\u00a0are about the key strategic questions you are seeking to answer and the quality of\u00a0the data you use to answer those questions. So before we dive into the various key\u00a0analytics let\u2019s step back and get really clear about the types and formats of data\u00a0that can now be analysed. Data types and format When it comes to data there are a few key distinctions that are important to understand.<\/p>\n
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\u00a0a defined record or file. This includes data contained in relational databases or\u00a0spreadsheets. 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\u00a0and analysis not everything in business fits neatly into a predefined field and that\u2019s where semi-structured and unstructured data comes in.\u00a0It is estimated that 80 per cent of business-relevant information originates\u00a0in unstructured or semi-structured data.<\/p>\n
And essentially it\u2019s everything else that can\u2019t be easily inserted into fields, rows or columns. It is often text heavy\u00a0but may also contain dates, numbers and different types of data such as\u00a0images or audio files. Semi-structured data is a hybrid of unstructured and structured. This is data\u00a0that may have some structure that can be used for analysis but large chunks are\u00a0unstructured. For instance, a LinkedIn post can be categorised by author, date or\u00a0length but the content is generally unstructured. Likewise, word processing software\u00a0includes metadata detailing the author\u2019s name, when it was created and amended\u00a0but the content of the document is still unstructured. Of course, the source of data is also important because most businesses are\u00a0already data rich.<\/p>\n
The problem is they are insight poor and don\u2019t often know how\u00a0to use the data they have, never mind utilise the treasure trove of external data\u00a0that also exists. As a rule of thumb, internal data is usually easier and cheaper to\u00a0access 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\u00a0business which is held either publicly or privately by another organisation. If data\u00a0is public then you can collect it for free, pay a third party for it or hire a third party\u00a0to collect it for you. Private data is usually something you would need to source\u00a0and pay for from another business or third party data supplier. External data might\u00a0include weather data, social media profile data, trend data or government-held data such as census information.<\/p>\n","protected":false},"excerpt":{"rendered":"
Analytics is something every business needs to stay competitive in today\u2019s datafilled world. Every manager needs to at least understand the basics of analytics and when and where to apply it.\u00a0It is impossible to open a leadership or management journal without reading\u00a0something on the explosion of \u2018big data\u2019, \u2018analytics\u2019, \u2018business intelligence (BI)\u2019, knowledge management\u2019, \u2018data […]<\/p>\n","protected":false},"author":6541,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-27984","post","type-post","status-publish","format-standard","hentry","category-howto"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/posts\/27984","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/users\/6541"}],"replies":[{"embeddable":true,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/comments?post=27984"}],"version-history":[{"count":6,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/posts\/27984\/revisions"}],"predecessor-version":[{"id":27995,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/posts\/27984\/revisions\/27995"}],"wp:attachment":[{"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/media?parent=27984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/categories?post=27984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/people.utm.my\/nurazaliah\/wp-json\/wp\/v2\/tags?post=27984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}