In addition to ensuring that business analytics technology is the right fit for specific end users and an organization as a whole, IT and BI teams need to make sure that they‟ve fully addressed data management issues before deployment.
In the case of in-memory analytics, data governance policies need to be put in place in order to ensure that data definitions,
dimensions and calculations are consistent.
Proper change management procedures are also critical, from both a technology and business-process standpoint.
That means putting enough thought and resources into training and supporting end users so that an analytics investment pays off in terms of adoption, usage and business results.
Change management to incorporate data analytics is often underestimated and underfunded. Implementing a data - evidence culture and training end users to use the tech and processes is very important to get a full ROI from data anlytics.
Brother Miller is right - appropriate change management planning and investment is absolutely crucial for both tech and business-processes.
If change management is underfunded and under-resourced - at worst analytics projects can fail and at best you do not realize full ROI.
Tough to get adequate funding for a real change management strategy- c suite thinks a few training programs all that required.
A few training programs does not work - from hard experience.
Tech and processes must be planned and deployed with great thought in close partnership with end users.
Change management is a major project with training one part. To get full ROI you need to change the culture - knowledge process and decision making redesign is hard and time consuming.
End users not only need to know how to use the tools but to gain insights and make better decisions via data analysis and evidence.
It don't come easy.
It don't come easy. Nothing great is easy. A fully invested change management program is critical for data analytics.
Amen - change management to incorporate data analytics tech and processes is freakin hard.
People do not like change. I suggest you change the incentive structure to help folks change.
Instead of trying to make decisions based on past experience or managerial intuition, business users need to learn to trust what the BI and analytics data tell them – then add past experience and managerial intuition to the business decision analysis mix.
This entails a shift in organizational culture - only a strong change management program and incentives work when attempting to change the culture.
It is important to stress that BI and data tools do not replace experience or managerial intuition in making decisions. They are tools to augment human decision making.
But the analytical decision making process changes and end users must learn new analytical techniques and learn to trust what the data and evidence say.
This is tricky - people do not like change and human nature is more comfortable giving greater weight to experience and intuition than facts and evidence.
This bias must be overcome before BI / data analytics provides ROI and creates the expected competitive advantage.
Considering the trend to using a myriad of data analytics tools, the organization's business intelligence standards could affect choices of data analytics tools. Organization's that have adopted a specific BI suite as a standard should carefully consider the implications of buying data analytics tech that's outside of their designated standard.
They need to look at what the issues would be of having another technology and another data stack.
You must coordinate data between different BI and data analytics tools - that usually creates complexity and complications for IT and end users alike. This takes time and brain damage - but the rewards are worth it according to the data science teams.
Then you need to do much testing to work out kinks before you deploy.
IMHO big data is overhyped. We have more data but no efficient way to simplify and gain true value from big data.
The tech evolution towards better value from the data is still in progress. Tech should simplify the big data into a model that is consumable by humans - transforms the data to actually represent something that can be analyzed.
The only folks who can make sense of big data today from the current tech is the data science mafia - not the common business user who has to make decisions every hour.
Rich - you make good points - the tech is still evolving and has a ways to go - especially for front line end users.
But it is equally true that the rewards and ROI from BI and data analytics are game changing. It has been done and is being done by many organizations - both in the private and public sectors.
Examples are Wal-mart; Goldman Sachs; Home Depot; US Department of Defense...... and many more.
The BI tech will get much better in the future but I suggest the time to start paddling to catch the next big wave is now. If you wait too long to start paddling you could miss the wave - and be at a serious competitive disadvantage.
Would it be a good thing to offload analytics from the data warehouse?
Great point - the data warehouse is not optimized for analytics.
If you got all of this analytics in your data warehouse, wouldn’t it be good to put it in an environment that is actually optimized for analytics? Perhaps. The data science team thinks so.
Offloading analytics from the data warehouse can boost your overall analytic capabilities. The traditional BI environment and the data is optimized for reporting for traditional querying and for slicing and dicing and that’s not the same thing as focusing and optimizing on analytics.
What we are seeing here is the traditional long term tradeoff that we have always done between specialization and generalization and the emergence of analytic platforms and specific analytic functions gives us the ability to do another piece of specialization and moving from that generalized platform.
There are new areas of analytics and new analytic functions coming up day by day - for example revenue assurance, fraud detection, managing customer churn, marketing campaign optimization, market basket analysis, customer insight. All of these things can stretch and stretch standard relational database.
Here we have the possibility when you think about analytic platform to do some of this work on a platform, which has been optimized and designed for that.
Another reason to offload analytics is the speed to speed the time to analytic results and from there to innovation. So if we have got powerful analytic platform we are going to get quick access to data. We are going to get access to advanced functions. We are going to get highest query speed and all of that gives us the ability to go more speedily through iterations of the analysis and get quicker time to resolve.
So think about it this way. You get data and it is somewhere in a database, somewhere in the operational systems or on the network or in the big data whatever it is, it is recalled. The first step is to make it usable and useful. You condition it.
The next step after the top of the circle is to utilize it, which means let me do some analysis on it, let me understand whets going on, let me pose some questions, let me think about some possibility and normally what happens at that stage is that rather than going to decision and action is that you discover, hey I actually need more data or maybe I need to go talk to my peers and you go around the loop and you assimilate.
If you can iterate here more quickly in your analytics environment, what you do is you get more quickly to the innovation and the business answers that you really need and this is a key point in terms of how we can imagine doing analytics as we go forward and have the platform that really supports us.
Offloading analytics from the data warehouse could also extend the life of the data warehouse environment, because we have taken the analytics off and that we can allow the current data warehouse environment to be maintained at its optimum query for performance and usage.
We can perhaps reduce the load on ETL for the data warehouse by routing the analytic data direct to the analytics platform or moving the analytic functions to the data where it lies and removing the analytics actually provides a growth capacity from the traditional workload on the traditional data warehouse environment.
I agree offloading analytics from the data warehouse is a great idea and will make the data science team very happy.
When moving data to a new platform you need to consider the speed of actually getting the data into the environment, the speed of design and implementation and cost (price performance) and complexity issues.
Are we reducing the data movement, or are we getting faster data movement, are we getting a design and implementation phase that is easier for us to manage?
We got to think about the data - the analytic environment and the analytic tools are still at the early stages of development and evolution. Organizations know their data and the analytics they need to do are probably pretty industry specific and organization specific.
So you need to do a proof of concept. You need to figure out is this going to be the way that you are going to do it and will it work for you on this platform and will this platform be fast enough for you and of course it is not only about performance, it is not only about speed, it is of course about price performance.
Agility of the analytics platform is an issue. How agile is the complete solution?
How easy is it to deploy new analytics, new tools, new functions, and new ways of analyzing the data?
Can we run many scenarios at once, can we run many advanced functions, how can we bring all this together into an environment, which provides our users, our business analysts with the ability to play with the data in a way that they want to do it?
Can we bring in the data or analytics when we need it? Is it easy to load it in? Can we easily load it in and quickly load it in?
How much agility do you need because like speed there is a tradeoff. The more agility that you want probably the more expensive and complex the hardware you need. So it becomes a bit of tradeoff but it is important to err on the side of more agility because it is very much an evolving environment.
Yes, it is very important to err on the side of more agility.
BI and data analytics is such an evolving environment - you want to be able in the future to deploy new data analytics tools and functions.
The data science team is always thinking up new ways of analyzing the data and that requires the incorporation of new data tools.
Scalability of the data analytics platform is another important issue. Can it handle:
Any amount of data?
Any depth of analytics?
Any combination of analytics?
Any kind of analysts?
Any number of analysts?
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