Last week, I had the pleasure of attending IBM Vision 2015 at the Hilton Bonnet Creek in Florida. Perficient was a sponsor of the event. This was my first time attending IBM Vision, I have been to a number of the other IBM conferences, and I came away impressed. Unlike massive events such as Insight and IOD, Vision is quite small and intimate. At Vision, it is easy to interact with attendees and presenters and to get around to see all the exhibitors. It was great to be able to check back in with people to see what sessions they had enjoyed. This year, IBM Vision had five tracks into which the material was collected:
- Financial and operational performance management
- Sales performance management (SPM)
- Financial close and disclosure management
- Governance, risk, and compliance
- Cloud based solutions for business insight
I chose to concentrate on presentations in the Sales Performance Management track.
Defining the Problem
For any growing organization, with a good size sales team compensated through incentives for deals and revenue, calculating payments becomes a bigger and bigger challenge. Like many organizations, Perficient handled this problem with Excel spreadsheets, long-hours, and Excedrin. Our sales team is close to a hundred strong and growing 10% each year. To help reward activities aligned to our business goals and spur sales that move the company in its strategic direction, the Perficient sales plans are becoming more granular and targeted. Our propensity to acquire new companies jolts the sales teams size and introduces new plans, products, customers, and territories. With Excel, it is almost impossible, without a Herculean effort, to identify whether prior plan changes had the desired effect or what plan changes might cost. With, literally, hundreds of spreadsheets being produced each month the opportunity to introduce errors is significant. Consequently, executives, general managers, sales directors, business developers, and accountants spend hundreds if not thousands of hours each month validating, checking, and correcting problems. The risks involved in using Excel are significant, with an increased likelihood of rising costs for no benefit, and limited ability to model alternative compensation scenarios. Continue reading
Everyone is guilty of falling into a rut and building reports the same way over and over again. This year, don’t just churn out the same old reports, resolve to deliver better business intelligence. Think about what business intelligence means. Resolve, at least in your world, to make business intelligence about helping organizations improve business outcomes by making informed decisions. When the next report requests land on your desk leave the tool of choice alone, Cognos in my case, and think for a while. This even applies to those of you building your own reports in a self-service BI world.
Think about the business value. How will the user make better business decisions? Is the user trying to understand how to allocate capital? Is the user trying to improve patient care? Is the user trying to stem the loss of customers to a competitor? Is the user trying find the right price point for their product? No matter what the ultimate object, this gets you thinking like the business person and makes you realize the goal is not a report. Continue reading
Business Intelligence should help organizations improve business outcomes by making informed decisions. The problem is that Business Intelligence is the overarching term applied to the tools, technologies, and best practices that that supposedly help organizations make sense of data. Where should you start? What tools should you use? What are the best practices? How do you manage the mass of data flowing into your organization? To which buzzwords should you pay attention? Perficient’s Enterprise Information Solutions group helps organizations determine how to put business and intelligence back into Business Intelligence.
In previous posts, I looked at Business Intelligence and what it means today
and Buiness Intelligence – Future trends
. In this post, I look at implementation challenges and how they might be addressed. The current state of business intelligence and the future trends, present a significant set of challenges to organizations trying to improve and leverage the data they have. Perficient’s Enterprise Information Solutions Company Wide Practice helps organizations do just that. The challenges that organizations face fall into a number of key areas.
Business Intelligence should help organizations improve business outcomes by making informed decisions. The problem is that Business Intelligence is the overarching term applied to the tools, technologies, and best practices that supposedly help organizations make sense of data. Where should you start? What tools should you use? What are the best practices? How do you manage the mass of data flowing into your organization? To which buzzwords should you pay attention? Typically, Business Intelligence is a technical implementation driven more by speeds, feeds, and glossy brochures. Perficient’s Enterprise Information Solutions group helps organizations determine how to put business and intelligence back into Business Intelligence. This post tries to define what Business Intelligence means today.
As 2012 ends, I thought it would be interesting to look at a category of tools that is gaining a significant hold in the Business Intelligence (BI) world. These tools are Personal Analytical Tools. The design of these tools allows users to be self-sufficient, to explore data, and to analyze it without IT involvement. The concept of user self-sufficiency is not new but the new tools are finally allowing users to realize the goal.
Recently, a colleague asked me if I had a list of solid probing questions about the state of business
intelligence within an organization. Moreover, because I was at the time part
of our IBM Business Analytics Practice, he wanted to know if I had questions
that specifically related to the IBM stack. What he wanted to do was to use the
question to understand the issues companies were facing in order to help
construct appropriate solutions and bring the right tools to the table.
I must say that I made a rookie mistake,
after 25-years in the industry, and the product focus derailed me. I
immediately started to ponder what questions could I ask about IBM Cognos
versus SAP Business Objects versus Oracle Business Intelligence and got
nowhere. I forgot that it does not matter if it is SPSS or SAS, or TM1 or
Hyperion, they can all for the most part do the same things. What matters is
what are you trying to achieve, and how do you want to achieve it.
Here is my list of top-ten questions to ask. At this stage, I am not going to try to
interpret the answers. If you can get honest answers, you should be in good
position to evaluate which tools are right for you and not end up with
shelf-ware and dissatisfied end-users. I am going to make one big assumption on
which to move forward. You have or can get access to the data you need.