Plug and Play Business Intelligence?

Really? Do I have to revisit this topic? Again? 2 years ago, I wrote that there is no such thing as plug and play business intelligence. Here we are and has anything changed? The answer is a resounding NO! 

Now, let me clarify this. Analytics tools have come a long way. There are several good analytics tools out there that allow you to do quite a bit of reporting and analysis yourself.

Then why am I insisting that there is no plug and play in BI? Unless you are the Data Warehouse Ninja , ETL(extract transform, load) Samurai and you have a team of 47 Ronin who, through the years of wisdom in the scrolls given to you by the smartest kid, I would venture to say that you don't have a handle on all the data that the business is looking for. And even when you do, the requirements are ever changing. Get my point? It's the data!!!

Data integration and standardization across the enterprise is going to be your biggest challenge. Unless you have that under control, you can put the next Alien ("look it also makes me a sandwich") dashboarding tool on top of it and you will still get bad data. Ever heard the saying, "all that glitters isn't gold"? Well, all that creates dashboards isn't BI either.

Omnition partners with United States Preventive Medicine

Omnition is pleased to announce that we are partnered with United States Preventive Medicine.
http://www.prweb.com/releases/2013/11/prweb11306930.htm

Big Data

Recently, I copy-pasted a link to an article saying that Big Data is dead. That got me thinking. Is it really dead? Serves me right for copy-pasting a link! Here are my two cents on Big Data. Most people don't have Big Data. Google has big data. Yahoo has big data. But if you are a regular business, and even if you have large amounts of data, you might not need to apply big data principles. Let me clarify. If you have large amounts of "unstructured" data and you have a need to mine them, then you might have a need for applying big data principles (Dr's notes, for example) . If most of your data is "structured", no matter the volume, you may never have to apply big data principles. If your data is mostly structured, even with large volumes, it is possible to extract information in a timely manner and you may never have to use big data principles. 

To predict or not to predict?

Let's face it, if you have been to any healthcare industry conferences lately, you have heard about analytics and more importantly, "predictive analytics". Wow! Can you imagine the power of "prediction" at your fingertips? What if you could tell the number of people who are going walk through your door any given day before it happened? What if, you could tell the number of patients who are going to be cancer patients in the next few years? That would be awesome, wouldn't it? The question is, would it?

Let's take a scenario....
Let's say that I now have the predictive capability of knowing that 3 out of my 10 patients will get cancer in the next three months. Great! Which 3? What type of cancer? What can I do to prevent that from happening? The answer is, probably nothing. It is not specific enough. While there are some general changes you can make to your treatment plan, the results will likely be a hit or a miss.

So why spend so much time, effort and money on prediction? To me, a better scenario would be a retrospective inspection of what caused something to happen. In this scenario, you can drill down to the specific root cause of what went wrong and it would be specific enough for you to be able to fix. Much better, "measurable" ROI, in my opinion.

So, when choosing to go after predictive capabilities, understand your goal for it. Look to see if there is a measurable ROI for it. If not, don't try to predict, analyze.

Sometimes, even we have to brag a little....

Ok, so I am not the bragging type, but this referral from a customer is something I had to share :)
“Using Meta Analytix’s cloud based Max on Demand helped us integrate data from two different hospital systems cost effectively. We were able to generate quality analytical reporting with very little initial investment in Data Warehousing and Business Intelligence technologies. We were confronted with a very tight deadline to quickly produce quality analytics and Meta Analytix was able to deliver right on time and within our budget.” –  Executive Vice President, North East IPA Group

Functional Analytics - Data Warehouse or Data Mart?

Data Warehouse or Data Mart?
So, here is the age old question. Should I go the Kimball route (data mart first) or the Inmon route (Enterprise Warehouse first)? While both have merits and demerits (we are not going to get into that discussion here as it has been done before). I think, a "functional" approach should be taken to maximize value. Let me explain. If you look at a physical warehouse, let's say, the warehouse of a shipping company, you don't see one warehouse for canned goods and another for toys and another one for perishables. You may see sections carved out for canned goods, toys and maybe a refrigerated section for perishables. But rarely do you see three separate buildings for each type of item. So why should it be different for a Data Warehouse? Basically, if you look at the data warehousing concept, we will see data marts that are "specific" to a line of business and a warehouse who is the closest thing to a physical warehouse, but often times so scattered and/or so complex that the LOB (Line of Business, if  you didn't already know that) seldom finds use for it. So are we stuck? Not really!

A Flexible, Extendable model.
What if you can have the best of both worlds? What if you could rapidly deploy a warehouse, knowing that there is going to be more LOBs added later?
What if, we could deploy this warehouse, knowing that tomorrow, "perishables" may be added to the warehouse and the model has to be adept enough to add "refrigeration"?
What if, you could add more information at different levels of granularity to the same model?
And...the kicker....what if, we could correlate information across the enterprise, with the same model and don't have to create LOB specific data marts?

"Hogwash, won't happen, what did you smoke this morning?" you say?

"Functional" Approach
So, if you are working for an organization whose primary business is NOT building software, then, you, my IT colleague, is there to support the "business user". I have yet to see a business user who has come to me and said "Boy, I wish I had a data warehouse". All of  them, however, have asked me one thing. "I wish I had good quality information. And I need it today, if possible". So why not give them just that? Rapid Deployment, Ability to add more information later. Can you design such a model? The answer is a resounding YES!

Has it been done before?
Do I have to repeat myself? I did say YES, didn't I? And I added the word "resounding" to give it a dramatic emphasis! Yes we do have such a model. Unfortunately, I can't share the specifics of it just yet without an NDA or holding your first born hostage. Our FlexDimensional model does just that. It allows us to do a few things:
1. Rapidly deploy with limited up front information (business process, granularity, fact etc)
2. Add more information without having to build new star, snowflake, or whatever else schema
3. Correlate enterprise wide information in a single, extendable model.

So, there you have it. There is always a better way of delivering "good quality information" at the point of decision making!

ICD9 - ICD10 Conversion Assistant - MxConvert

Ok,
So this is pretty slick and I have to tell you about it. We recently launched an ICD9 - ICD10 conversion assistant tool called MxConvert that works within Excel. This is geared for you Code Warriors in Healthcare who have to undertake the massive task of converting ICD-9 codes to ICD-10. Take a look:
http://www.metaanalytix.com/page.php?page=38