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A Race Towards Pervasive Analytics
Gartner’s top 10 trends for 2010 set the stage for Cloud computing and Analytics.
Analytics is context driven, and presents actionable results to the business user. BI allows the user to slice and dice data. BI is good if you know what you are looking for. The reason Gartner placed analytics above BI is because of the needs of businesses today to act on data, as opposed to merely having access to it. There is way too much data. We do not need systems that create more data – we need intelligence from the data, which is what Analytics does. Hence the positioning of Analytics on the Gartner charts.
BI has become pervasive, as it should be. It has even entered open source with Pentaho and Jaspersoft, a sure sign of being pervasive! However, this was inevitable as every business user needs easy access to their data. A recent survey conducted by B Eye Network involving more than 1,000 respondents from around the globe found that only 12 percent said they had no plans to use open source software in some form for business intelligence applications or data warehouses.
However – converting the data to intelligence, and actionable intelligence in the next frontier. That is why Gartner placed Analytics in their top 10 trends chart, and moved BI out! As we have watched with Google analytics, the analytics on web traffic data is pervasive. There are a myriad of products that provide analytics on web stats, but Google provides a universal product ensuring that everyone has access to it.
Analytics on corporate data will also become pervasive. Companies are demanding this. The analytics will be contextual, as this is required for analytics to automatically make sense out of data. The analytics will be agile and companies will be able to pour their data in, and watch the results take shape. Much like Google analytics on web traffic data.
Emcien’s analytics offers analytics on sales data. The context is sales and customer buying patterns. Companies can now pour the sales data and watch the customer buying patterns emerge. No data mapping and model building. No long implementation cycles! The ability to “just turn on and use” is key to being pervasive.
The future is here!
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Why the need for Intuitive analytics?
I just finished meeting with Kathy, a customer who is very analytical and has done an amazing job delivering value on data intelligence for her company. I spend an hour with her and she described the process of implementing tools to gain value from data. “This has been a painful process of exploration” she said. “We are a team of analytical types, and we love to play in the data. Not all companies can afford a team like this. In my last company we could not afford it. And companies cannot compete effectively if they cannot gain intelligence from their data!”
I asked her the challenges with the project and lessons learned. Kathy is a data genius, and there are very few folks like her. Here are some of the points that she made.
- “What am I supposed to do?” The analytics tool needs to answer this question for the business user. It also needs to be very easy to use and understand. This comment reminded me of another conversation with a BI user, and his pet peeves with his BI tools – “I do not know what question to ask!” He said. “Asking intelligent questions is most of the task!”
- Tools for Business Users. Not R&D. Not IT. Kathy went on to comment about the dire need for tools for the Business Users. Companies are running very lean. The last rounds of corporate downsizing have been brutal. Typically, the first teams to go are the analysts and R&D teams. Hence, if the tools are hard to use, now they will never get used. Business leaders are getting smart and they want tools that their line-of-business folks can use. This can help them get value in the line of business.
- Actionable Results. The old slice-and-dice data capability is not useful. Ask may questions and I will answer. The business units are too busy and do not have time to “play in the data”. The tools need to intuitive and give you actionable tasks.
- Like Google Analytics – The words “Analytics” is always associated with webs stats. That is the most commonly understood context of the word. However, there is far more data residing and building up in companies, than there is web traffic and search strings data. We need the same level of analytics, easy to use and intuitive tools for corporate data as we have for the web stats. Analytics means computing a relevant result and presenting easy to understand solutions. Like Google analytics for web traffic. Google analytics presents analytics on web stats; very domain specific, turn it on and off you go! Analytics for companies needs to be like that. Domain specific. Turn it on. Go Live. Get results. Get value!
- Need to think like B2C apps. This was an important point. Why do business users have ugly, clunky software that is expensive to implement and difficult to use? How can they be effective making those million dollar decisions? When the new wave of kids, who grew up with iPods and Facebook, enter the work force, will they throw out all the clunky enterprise apps? Absolutely!! Replace them with with easy to use, Facebook like apps. Business analytics needs to be that easy to use.
Great opportunities! We need to build applications with the user in mind. What will business applications learn from iPod, Facebook, Linkedin, ……
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BI is for IT, But Analytics is for the Business User!
I’ve just come out of a meeting with a business user who is passing up on a well-known BI software package. “It takes too many IT resources to implement. My IT guys love it but I cannot afford the cost/time,” he said.
As companies drown in data, BI is a very expensive route to try and gain value from the data. Mark McDonald, head of research for Gartner Executive Programs, has a very nice article titled Without the Business in Business Intelligence, BI is Dead!. Sounds like – “The King is Dead. Long Live the King.”
BI has been built for the IT community. It is an old-school solution built on the heavy weight model of technology. That model rests on the acquisition, installation and operation of technology based on a significant upfront investment that is earned out over a period of time. This has been the investment/implementation path for enterprise ERP/SCM/CRM/PDM software packages. BI is in this class of software solutions, and results in an expensive and less responsive solution. Mark McDonald calls this the “old ‘heavy weight’ model of technology”.
The top two categories for Gartner’s predictions for 2010 are Cloud Computing and Analytics – these are both directions that are a far cry from the old world of heavy/expensive/pay upfront software. BI in its current form is completely out of step with these predictions and where the market is heading. So, why did Gartner renamed BI to Analytics? In doing so is BI going to magically transform from its old heavy weight form to a new lean enterprise 2.0 form? Long Live the King?
Emcien offers pattern-based analytics that easily takes sales data in any form to reveal customer buying patterns and trends. The technology has completely eliminated the need for data models, structures, mapping, etc. Emcien’s pattern based analytics technology was created explicitly to overcome the ‘old heavy weight model of technology’. Pour your sales data in, and watch the customer buying patterns. “Like Google analytics for sales data”, our customers told us.
Mark McDonald has prophecy for BI that I think is dead-on (pun!?). “On a radical note, we are seeing some early signs that companies are looking to use social media/web 2.0 technologies to address business issues that were previously assigned to BI.
Lighter weight technologies handle tacit information and semi-structured process support better than BI solutions that rely on structured and standardized information.
Our customers completely agree with Mark McDonald. Quoting a VP from a Fortune 500 company, “We have lost the appetite for million dollar software and long implementations that consume IT resources”.
However, the need for harvesting intelligence from data is not going away. On the contrary, it has never been more important than it is now. The data hides jewels of intelligence that companies need to act on NOW. But that is only possible if Business Intelligence is not a technology but a capability for the enterprise. Long Live the King!
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Part II: Analytics to Action….. The Holy Grail
Can-you-have-too-much-information?
… in my last blog we talked about reporting, BI and data mining, and ? the information overload. So how can help business users with solutions for better decision making, as opposed to drowning them in more data and pretty charts? That is the Holy Grail and the purpose of all this data!
Lets start by defining analytics. So, what is analytics? Neil Raden of Hired Brains, a market research and management-consulting firm, has said that, “the proper term for interacting with information at the speed of business, analyzing and discovering and following through with the appropriate action, is ‘analytics’. I agree. In the information age, this must be done by specialized applications built on analytics based on the requirements of the actions/recommendations required by a business function. Dumping data on a users lap with the message – “Figure it out!” is NOT analytics, and it not very useful either. (I am reproducing a picture I really like as it conveys the message so very well! The Picture is from mathewingram.com/work)
So – how can we transform the user experience for analytics? As mentioned earlier, this can only be accomplished by focusing the analytics on a business problem with the mission to deliver actionable tasks. The challenge is selecting a business problem that the analytics truly delivers unique capabilities and intelligence that is relevant to that problem. This level of focus can be perceived as very limiting, and hence many choose not to go this route. Why limit the scope of the analytics to one specialization, when we can claim that we can do everything! To that I say – you are better off doing one thing very well, as opposed to many with mediocrity at best.
I am going to bring this back to Emcien, as this is a company that has focused analytics on a very specific business problem. The problem is one of product variety, product variants, and lots of attribution. In this age of product variety, that is a problem that is causing tremendous challenges to various business functions.
The analytics automatically detects what features customers are buying, where you are making money. This SKU or configuration intelligence is leveraged for:
SKU Intelligence Analytics Used to Drive Application Specific Recommendation
- Better forecasting at the mix level - The application uses the analytics intelligence to determine the exact product mix with very high accuracy based on true demand sensing.
- Improving the customer experience at the point of sale - The application uses the analytics intelligence and guides the buyer to a good configuration based on the few features they have called out. And by the way – customers love it when you can recommend a configuration based on the few features they ask for. They want you to stop asking more questions and recommend a good choice.
While the analytics may throw out volumes of data, the user can relax, as he does not have to crawl through volumes of date wondering what it is telling him. Converting analytics to actions and recommendations minimizes human interpretation and error on a day-to-day basis. For analytics to be functional in business applications, this is a mandatory requirement in today’s business environment.
So – when you are evaluating BI tools, Analytics, Data mining….. what ever they are calling it! Ask yourself, how am I adding value to the company? What am I giving my business users? Am I adding more work to their busy schedule by piling on data on their computers???? If the answer is YES, please don’t do it. They will thank you for it.
If the data has not been converted to recommendations the business can act on, you will not get value from your investment!
Part I: Reporting, Business Intelligence, Data Mining, Analytics: Actionable Tasks!
Software vendors use so many big words and confuse customers. Our customers have often asked us to clarify – so here I go. The definitions in this article are based on research of these terms, and the collective opinion of many of our customers and prospects. Over numerous conversations with our customers and the discussions of the terminology, the clarifications always go back to the origin of the terms and then move on to change in usage. Hence this article folows that flow. I would love your feedback as it is important to help buyers understand this.
Business Reporting
Business Reporting, as the term suggests presents the data from the database in an easy to read format. This originated when business users were frustrated that all the data was locked up in databases. There was a lot of data, but no one could get access to it without calling on IT folks. Hence Business Reporting was born.
Business Intelligence
This is a fancy name for business reporting. Business intelligence (BI) is a broad category of technologies that allows for gathering, storing, accessing and analyzing data to help business users make better decisions. In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”
In 1989 Howard Dresner (later a Gartner Group analyst) proposed Business Intelligence as an umbrella term to describe “concepts and methods to improve business decision-making by using fact-based support systems.” Then in the late 1990s the usage became widespread (Remember the Bubble!). Then. everything with any data reporting was called Business Intelligence. So today, Business Intelligence is a glorified term for “Business Reporting”.
Data mining
Simply put, Data mining is hitting the data with all mathematical methods available to a mathematician! The data source can be almost anything – news papers articles, financial reports, sales data, medical data, … . This means that the data can have structure or can be un-structured. And the mathematical methods that can be applied can include neural networks, genetic algorithms, statistics on steroid and anything else they can think of.
One may ask – why are they doing this? What are they mining? Well, the simple answer is that they are mining the data looking for patterns; any patterns that can reveal relationships. So the methods used are varied and the kinds of data that are mined can come from a myriad of sources.
The results of data mining are lots of data! In fact – the result of Business Reporting and BI has been data overload. Now that’s the bad news. In a world of information overload, the last thing that we need is more data. We have less time today than we have ever had before. Business users do not need more data. They need quick conclusions on what the data is saying, converted into actionable tasks. Simply put – “Please tell me what to do”.
… More on the discussion of analytics to action in the next blog.
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The quality connection
Recently I wrote about quality rankings for automotive manufacturers and the perception of these rankings in the market. While the marketing teams at these companies must shoulder the burden to convince consumers about their products’ quality, there is a very real connection between product quality and configuration management.
In many industries where products have grown over time with constant additions of new features and flexibility to allow customers to build to order, the level of complexity is staggering. Often the number of configurations sold on an annual basis is surprisingly close to the total units sold for that same period. This “snowflake” situation is one of the worst possible scenarios in product complexity as each unit has its own signature. Obviously, the production of these products also requires flexibility in manufacturing. This may result in reduced use of automation, and often it leads to units being reconfigured where components installed during one step are either removed or modified in a later step due to a unique situation.
These one-off manufacturing processes open the door for product quality issues due to fewer controls during production. Put simply, if I can reduce the number of different things that must be done during production I should be able to do those things better.
So product management teams have direct input on product quality via product complexity. Managing the product option mix to reduce the overall number of configurations can promote the increased quality that all manufacturers are looking for.
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