The term ‘Analytics’ is becoming more and more commonly used…
Analytics are also the end game of the BI System Builders’ Cornerstone Solutions®. But what are Analytics? This article describes the term as used by the BI System Builders. While some consultants in other organisations may have a different definition this paper will serve to make the BI System Builders thinking clear.
Firstly, it’s important to note that the term Analytics is frequently used interchangeably with other names such as Analytical Applications, Business Analytics, and Performance Management Applications to name but a few. BI System Builders only use one terminology, that of ‘Analytics’.
Now, all organisations capture data. This may be through sales invoices, captured in a call centre, via a website, delivery notes from suppliers, research questionnaires, financial transactions, point of sale transactions and so on and so forth. The data captured is in a raw format and will usually be disjointed across several of your data capture and transaction systems. At this stage data viewed in reports is often referred to as operational reporting. The data in operational reports is not summary but low level, detailed and granular. That’s fine but have you ever tried to read through a million rows of data in a day? That granularity may have use for operational purposes and will be heavily filtered but data for BI purposes needs to be at a higher level, or consolidated, or summarised in some way to make it readable.
The transaction system data captured has been growing in high volume over the years. It may be structured or unstructured data and can also be highly volatile. Furthermore the exponential growth of the internet, mobile devices and social media has meant the potential for enormous amounts of detailed information to be captured everyday – your web clicks, your buying behaviour, your communications, your location, GPS co-ordinates, your posts, your reviews, etc, etc.
Historically, Business Intelligence tools have been used to extract, clean and format the raw data then join it up together turning it into valuable and useful business information. That’s great, it’s relevant and highly valuable in a structured data warehouse. The data is often summarised and may also be pre-calculated in other ways as well. The information can then be used to discover insights into your business. The reports are sometimes shared across the organisational enterprise, hence the term, enterprise reporting.
Let’s take a look at the classic Analytic capability in the data warehouse and then consider Analytics in the new Big Data era. Analytics go a step further than simple reporting by adding extra business value into the reports. This is done in several ways. Some of the classic ways are the use of dynamic parameters for on the fly analysis such as period on period, ‘drill down drill up’ through hierarchy structures, a drill across capability linking buisness process areas across the business, and predictive engines or ‘What If’ scenarios.
Dynamic parameters allow the user to refresh the data in the Analytic and change the question being asked by selecting contextual values in a prompt. For period on period analysis you may start by viewing sales today compared with sales yesterday. The business user can then easily change the Analytic to compare current month to date sales versus last month to date sales or last year’s month to date sales etc. The Analytic below includes several calculated fields and all the analysis periods can be changed. When they are changed the other fields automatically recalculate. The Analytic also has a drill down capability and dynamic sentences which automatically capture values such as the name of the city with the highest percentage change.
Drill down and drill up is used to navigate through a hierarchy in your data. For example you may capture sales data by location. In an Analytic you may view sales for all locations and then with a click of the mouse ‘drill down’ to sales by region, and then sales by city and down to individual sales outlets. This can all be achieved within a single Analytic.
The drill across capability allows you to navigate through a series of linked Analytics. These are often based on a logical business process flow. The linkage is achieved through code written behind the scenes and invisible to the business user. Analytics are clever enough to know the context of the information you are viewing such as location, time period and product and pass this context to the next Analytic.
Predictive and What If capabilities allow the user to play out different scenarios. For example you could measure the impact of inflation. To do this a percentage value would be input into a dynamic parameter prompt. An algorithm in the Analytic would then read the value and recalculate itself to show you the impact.
There are many other things that you can achieve with Analytics such as cycle time analysis. The picture below is a cycle time chart created by BI System Builders using SAP BusinessObjects XI 4.0 Interactive Analysis (Web Intelligence). In this case the chart is analysing the customer order actual cycle time but the technique can be applied to any business process. The information has great value. If you can measure the duration of individual stages within a business process you can identify those that are least efficient. By then addressing any inefficiency within a stage you can make it more efficient, reducing its duration and consequently cost, thus improving the profitability of the process.
It is also common to measure things such as KPIs, customer churn, customer life time value, stock turn, and do time series analysis, and develop strategy maps, balanced scorecards, and six sigma based statistical process charts across all your business process areas, not just one process area in isolation. Analytics based on the BI system are enabled to combine HR data with Finance data, and Finance data with Supply Chain data and so on and so forth. The bottom line is that Analytics will help you find efficiency and effectiveness in your business according to your needs. This type of Analytic is currently used by many organisations although many still struggle to realise them. They are also demonstrated through Google Analytics, LinkedIn Analytics, and YouTube Analytics.
However, we no longer live in the stable and slowly changing world of the data warehouse alone. The cost of technology is reducing, the 64 bit processor is here and large amounts of RAM can now be exploited. Furthermore, technological advancements have yielded in-memory applications such as SAP HANA and distributed architectures and file systems such as Apache Hadoop. The combination of these things means that enormous volumes of data can be ‘released’ from the systems in which they have been captured and processed fast – near real-time, and real-time fast. Things that were once beyond budget are now starting to come within budget.
Earlier I asked the question about have you ever tried to read through a million rows of data? I wasn’t joking, I’ve witnessed business users attempting to do this is in a report and then find the bits of information that they needed. Ofcourse, they give up quite quickly and don’t get repeat opportunities because of the system degradation such report processing can cause. Now I’ll ask another question, “Have you ever tried to read though a hundred billion rows of data in a report?” Preposterous? – yes, impossible to process? – no, some organisations now capture data at the terabyte plus scale daily.
Hence the rise and rise of Analytics. It is impossible to make meaning out of such high volume, highly volatile and disparate data as is now becoming available without Analytics. And with such rich data available it will be exploited and it will be exploited in a pre-emptive way. Consequently the term ‘Data Scientist’ has been popularised and is closely associated with Analytics. As sophisticated as the new Data Scientists may be with algorithms and complex modeling the concepts of using decision engines, learning models, predictive engines, data mining, BAM, pattern identification, trending, time series analysis, correlation, regression, tests of significance, pattern detection, disparate source mapping, and identifying homogenous groups is not new. However, now the technology and the data is available these things will be exploited in Analytics like never before. For a clear example of this consider ‘Big Data means Marketing Science’, but that topic deserves an article all of its own…