For more than 25 years now, businesses have followed a pattern of mapping business transactions in their own context into a data warehouse. Realistically, this practice, driven by the ideals and methodologies of either Bill Inmon or Ralph Kimball, has to-date provided a great ability to re-format transactional data into a model that enables rapid retrieval and consistent enterprise reporting platforms. I believe that through this analytics evolution cycle, we have become a victim of our own design in that the value delivered from a data warehouse built in this manner is, by and large, not conducive to encouraging new questions to be asked of your business data. This stifled pattern of use leads us to, typically, answer questions from complex data warehouses for answers that we could just as well have built a static report from the operational system.

What’s the point you might ask, well, simplistically, if the business transaction from one area in your organisation looks like and apple and another looks like an orange, the reality is that it is difficult to compare or analyse the two together. There are a number of anomalies of which timeousness of information, granularity and the absence of common associative information are but a few that restrict us in doing this effectively. So, if we insist on making each warehouse entry look like the apple or orange from where it was sourced, the warehouse is, by design, failing to deliver much if any value other than migration of the reporting platform to a different system.

Some organisations have gone some distance toward standardising the context of the data warehouse(s) and have reaped the associated reward that comes within the data management and governance side not to mention having a somewhat common context and dimensional view across the business reports produced. But, and it is a big but, at the end of the day we are still joining apples and oranges of varying granularity levels as well as different structures. This said, where our thoughts have gone in CryspIQ is looking to resolve this has been to look into the truly successful operational application solutions and start analysing what makes them so good.

The new paradigm explained

Successful business applications have, without exception, one thing in common and that is, they are designed to deliver their primary function well. Makes sense doesn’t it. By example a financial system irrespective of brand is great at finance transactions and can probably be configured to carry some other functions finance related but it cannot ever effectively cover, for example, an application that does inventory management without significant extension. So why have we, in the Business Intelligence world, persisted in making our data warehouse(s) designs to keep trying to do analytical processing whilst keeping the records on a one-to-one mapping with the source? Surely we are just carrying the problems experienced in reporting across multiple different business transactions from multiple systems into one, albeit single, system where we are trying to apply some smarts to untie the knots? Ultimately we still end up in most circumstances with the same apples and oranges explained earlier….. This in mind, we at Crysp Pty Ltd from the isolated city of Perth, Western Australia have categorically started to turn the thought processes for Business Analytics and Information Management around. What if we were to ignore the existing source systems as a point of design for the data warehouse and were to instead look at the analytical requirements of our business as a start point? What if we could build a system that can deliver what we need directly from the repository, would that make a difference? The simple answer is ‘Yes’ . Try thinking of it this way, in order to simplify and get to the light bulb moments that my partners and I reached a couple of years back now: -

• Does the current typical analytics design methodologies and derivatives thereof have any inherent flaws – Answer – No – So, no need to re-invent the wheel here use what works!;
• Is the delivery platform of importance for delivery of analytics to where it’s needed – Answer – Somewhat yes but it is not restricted to any one tool or vendor – So, don’t discard investments in delivery channels and mobility, you can re-purpose the technology for use in a new paradigm;
• Is the analytical structure of an organisations data need already known – Answer – Mostly ‘Yes’ – So, you can normally re-build a successful analytics system using your existing people and knowledge with some guidance and often with your existing dimensional views;
• Is the data in its operational form and context able to deliver true analytics? – Answer – Mostly no as it is too contextually bound to its source and does not easily match other data in the organisation; and finally, the coup-de-grace
• Is there opportunity to rebuild/restructure my data warehouse to enable my organisation to achieve true information analytics in the business’s hands? – Answer – Yes, yes and yes again…

If you’ve seen it already, good for you otherwise, here it is: -

“Restructure an existing data warehouse whilst maintaining the organisations, already understood, dimensional context, delivers a system capable of providing analytics as a function rather than simply collecting records (often for the wrong reasons) and then trying to make sense of it.”

Some will say, we already do that and I would lay down one challenge to these nay-sayers to see if this is really true. If the business wants to add brand new content to the current front end analytics, what has to be done to make this happen? If your answer results in more than one item, then, chances are you are not yet where you need to be in your restructure efforts. The game really changes when you are able to truly drive your organisations analytical capability from the user side, not from often expensive and time consuming IT technical competency. We at Crysp Intelligence recognised this need and have delivered a flexible methodology where your organisation needs to only do one thing one thing to add brand new content to the analytics engine, just think how powerful that would be measured in financial and time to value terms?

Let me answer that with some actual cost and effort facts to show you the financial and time difference between the current model(s) and the new idea……

• Current costs of adding new content to a large corporate data warehouse environment ~$50,000.00 vs proven cost of ~$6,000.00 to deliver new data to the warehouse in the new paradigm.
• Current time to add new content to a large corporate environment ~ 3 Months (12 Weeks) vs timed delivery of 5 Days for the same content using the new paradigm.

Simplified into pure business speak, would you like to reduce your cost for new data in your Business Analytics /data warehouse engine by up to 88% or more? And would you like to see the new data content arrival rate in your analytics core increase by up to 92%? I would hesitate to say that the answer for any business has to be a resounding ‘YES’.

Couple this with the results of moving the business analytics responsibility into the hands of the business users and removing constraints typically applied through current database structures (remember the apples and oranges discussion) we believe that our new methodology will be of interest to you.