Five New Ideas From 2010 MIT Information Quality Industry Symposium

Here are some quick thought from the first day of the MIT Information Quality Industry Symposium. It’s my favorite event of the year. I refer to it as the “anti-boondoggle.” All academic theory and very little vendor fluff. I suppose that what you get when MIT and the University of Arkansas organize events. I’ll either post another top 5 tomorrow, or a full recap.

Please comment if you’d like me to dive further into any of these topics.

1) Cloud Is No Longer The Focus

Last year everyone talked about Governance in the cloud. This year it’s dead. Why? I think it may be that this group, unlike the Sales 2.0, is focused on Enterprise scale monolithic systems. Last year at MITIQIS, many presentations were focused on the cloud impact on large scale information quality programs. This year, it’s all about internal, installed systems. I find this facinating. Did this group try cloud, and not see the value? Or is it that there is still a duality of idealogies: One that prefers to keep things internal, and a second that wants to move their IT responsiblility to SaaS apps?

2) Master Data Management (MDM) Isn’t The Only Solution

I was surprised that among the Information Quality vendors and practitioners, MDM was no longer the focus. Joe Bugajski focused on it, but others merely touched on how they would interact with MDM rather than focus on MDM as the central system in an Information Quality focused environment. This year, many people talked about Information Quality at the system level, and fixing business process and human interfaces to eliminate dirty data at the source. This reminded me of the Data Warehouse to Data Mart paradigm shift of 10-15 years ago. I just felt old writing that.

3) Data Quality is a Dirty Word

“Information Quality” is now in vogue. I was corrected several times in conversations when I mentioned data quality. This is somewhere between a more highbrow way of marketing ourselves, and snobery. I don’t think this matters in the least bit, but others believe it’s more accurate and lends more credibility to our practice. As you’ll notice throughout my writing, I resist heavily the practice of pluralizing the word data. I never write, “data are,” which I believe is gramatically accurate. I feel the same way here. I do “Data Quality” work, regardless of who says that term is wrong. All right… I’ll use it in this post and try it on for size. This is the Information Quality Symposium after all.

4) Free Sources Drive Down R&D Cost

Data is available from government sources and tools are available from open source communities. No surprise there, but there was in increased focus on it here at MITIQIS. Why? Talend, an information quality vendor, builds their tools on the back of those open source libraries. They credited various shared data models, methodolgies and data sources that allow them to shortcut proprietary R&B spend. Trillium also spoke up, and mentioned that they leverage some of the same open-source thinking in their full price solutions.

5) 60-90% of Operational Data is Valueless

I won’t say worthless, since there is some operational necessity to the transactional systems that created it, but valueless from an analytic perspective. Credit to Kirk Amidon for this insight - he attended the session where this stat was quoted. Similarly, Steve Adler from IBM and others discussed it in their presentations. Data only has value, and is only worth passing through to the Data Warehouse if it can be directly used for analysis and reporting. No news on that front, but it’s been more of the focus since the proliferation of data has started an increasing trend in storage spend. That wasn’t discussed at the conference… just my opinion.

Sales 2.0 Conference Recap Part 1

Yesterday, I spent a whole day emersed in a world of new tools and technologies at the sold out Sales 2.0 conference. Now that I’ve had a chance to reflect on the day and the conversations, I wanted to present some of the hightlights and my opinions of the various topics discussed.

What is Sales 2.0? I’ve already tried to answer that question once, but let me give it another try. Sales 2.0 is a change to people, process and technology that allows sales and marketing teams to do their jobs in a more intelligent way. It is either very customer or productivity focused (depending on who you ask), but probably not both at the same time.

The theories presented and tools demoed were mostly focused on enabling Sales reps with information that allows them to move closer to consultative selling. However, the final session of the day was defined by the use of ConnectandSell, a smarter version of a dialer that allows reps to efficiently communicate with lots of customers. To gain that efficiency, reps are trained to “just start talking” when they hear a beep in their ear. While that may sound efficient and is likely cost effective, it is the furthest thing from Consultative Selling I’ve ever heard of. How can you be consultative when have no idea who you are going to talk to a second before the connect? Maybe I’m missing the point, but this seemed to be contradictory to everything else discussed at the conference.

Some highlights from the agenda:

Polly Sumner, Salesforce.com’s chief adoption officer was a facinating person to listen to. Polly is a veteran of several of the technology giants and is now helping SFDC get into more places. She is passionate about the technology, and couldn’t wait to demo an iPhone version of the app to an eager group. Her description of “Chatter” led me to proclaim operational sales reporting’s death. I also came to the conclusion that SFDC will lead to more tech savvy and analytically focused sales management over time. I’m sure I’m not the first to come to that conclusion.

Polly spoke about SFDC using their own tools and publishing their best practices for all of their clients to grab. While not unique, this approach arms her adoption conversations with real experience rather than Marketing fluff. Polly showed us how she would follow an account, opportunity, contact or sales rep using chatter to understand an account. By doing so, she no longer had to waste an hour with an account review to prepare for a client meeting.

Perhaps the most important thing I learned from Polly was to take an individual focused approach to implementing CRM. Make it simple for an end user to use a couple of CRM / SFA applications, and they will be more likely to ask for more. Present too much at once, and they will be overwhelmed. She also talked about enabling C-Level executives with Dashboards allowing them to see directly into their CRM system, which could be a revolutionary step that disrupts boardrooms in the future. Imagine the CEO having better information than the Head of Sales? That would be exciting to say the least.

Up next in Part 2: My panel on Sales and Marketing and the end of IT!

CRM and Marketing Measurement to Drive Sales

How do you drive the Sales team to follow up on all those leads you’ve provided? And how do you get your marketing team to provide qualified leads instead of rows of uninteresting data? The answer is to measure both and use the resulting reporting and analysis to find gaps in your marketing and sales processes.

Your marketing and sales systems do what they are supposed to. They keep track of your interaction with customers (CRM) and help your Sales force plan to sell (SFA). They also keep you in touch with your customers through some combination of inbound, outbound, email, direct mail, and other marketing services your company provides. You may have even integrated your social media, web analytics, and online media, but chances are that you have not.

So what’s next? Convergence time.

Let’s start with a basic CRM program and build on valuable layers of information along the way. My company sends out lots of email marketing, so that’s what I talk about more than anything else. Once I’ve executed an email campaign, the first thing I want to look at is the result of any of my tests. Did recipients open emails more frequently when I did A vs. B. Next I want to know whether people clicked through the action buttons I provided them. These first two points are marketing centric, but next comes the fun part.

Once I know who clicked through the email, I have a new segment of interested parties. This segment can be divided up into customers and prospects (based on data available in your CRM system). As a next step, what did those people do when they clicked through to your landing page or site? Did they look at one page and leave? Or follow a path toward a product that you advertised? Or follow a path to a different product? We have now added another level of advanced segmentation from your Web Analytics platform. From a marketing perspective, you can now define lead nurturing programs.

And from a Sales perspective, we have defined richly qualified CRM and Web segments. So what happens when you add a note to your SFA system letting the rep know that a contact or a prospect account has clicked through an email and pursued a specific product? If your marketing team has built credibility with sales reps and management, you can be sure that the rep will trust this information and follow up to drive a sale. If you haven’t built credibility (or communicated this new approach), then the rep will see another bad lead and do nothing.

Either way, you need to report on these cross functional results. When presented Marketing qualified leads rather than bulk leads, what was the action taken by each sales rep. How much revenue was generated for each lead delivered? What about for each lead worked by a rep? If you are able to, you should test delivering different segments to different sales teams to determine which teams are best able to drive results. Maybe inside sales has a better chance to make a qualified lead sale since they are available to follow up immediately when provided a triggered lead. Alternatively, the product path followed online may define the sales rep that should get the lead. Or it could come down to rules of engagement on who gets what.

Once you are able to present segmented results showing which teams follow up on leads and drive sales and which teams let leads sit, you can have better conversations with Sales and Marketing management. This will expose issues with both your Sales and Marketing departments, but can lead to conversations that help both organizations mature. Are your marketing teams communicating well and enabling the sales with the appropriate collateral to sell to these leads? Are your sales reps following the playbook and working the leads the way management thinks they are? Show the results and let the conversation lead to change at your company.

B2B Customer Intelligence Series - Introduction

My job title and primary focus is Data Governance, however the data I spend the most time managing is B2B data. As a former consultant, I constantly find myself suggesting new applications to drive Sales and Marketing programs. Sometimes it’s as basic as dipping into database reserves to find new prospects, but often my extracurricular projects are pure Customer Intelligence, also referred to as CI. Customer Intelligence can cover a lot of ground, so let me define it.

Customer Intelligence is the intersection of Sales, Marketing, and Analytics that helps present the best customers and prospects to inform company strategy and tactical approach.

Customer Intelligence work manifests itself as Analytic projects including predictive modeling, cross-functional leads programs, and Sales and Marketing strategy projects. In most companies I’ve worked with, Customer Intelligence is distributed across several functions and that group collectively defines the companies Customer Relationship approach. In others a central group focuses on Customer Intelligence and coordinates the distribution of related information to drive strategy.

Other companies have no Customer Intelligence. They are Customer Ignorant. In that case, the Sales and Marketing teams approach customers based on generic approaches and anecdotal history of which customers are the “best”. With good products and excellent customer service, this approach can work. However, over time it will open the door for competitors to take over and dominate the market.

Here are a few thoughts for the upcoming series. The plan is for each of these topics to be developed as an entire post over the next few weeks. If you have other suggestions, please comment on the post or tweet a reply to @markgoloboy.

1) Customer Intelligence Flexibility

No matter how successful it is, every sales strategy will be retired someday. It may be next week. It may be next year. But be sure, the target will be set somewhere other than it is today. How do you build a Customer Intelligence strategy that allows your organization to stay agile?

2) Aligning Against the Opportunity

Once properly implemented your Customer Intelligence approach should become an integral part of the sales reps’ day. How do you integrate the information with your existing processes and systems to align against the opportunity?

3) Direct Marketing Impact on Sales

Customer Intelligence can help define a set of high value prospects that deliver return on Marketing investment. Determining which factors influence purchasing requires advanced analytics. What’s the best way to develop analytic models and measure whether they predict a prospect’s likelihood to become a customer?

4) Defining Lifetime Value

Determining Lifetime Value (LTV) of a customer and potential LTV of a prospect can align Sales, Marketing and Product goals with corporate strategy. However, LTV analysis requires that your product and sales data has been collected consistently for your company’s history. How do you develop and use LTV to drive product direction and Sales and Marketing focus?

Pick The Low Hanging Fruit of Data Quality and Data Governance

This has always been my favorite terrible consulting / business cliche. I suppose I’m using this forum to solidify it’s status there, but I imagine many of you have been told or said something very similar. Of course this fits into my Lightweight Data Governance theory as well.

Saying that you are going to Pick the Low Hanging Fruit resonates with budget conscious managers and technologists who want to see quick results. It shows that you are unwilling to get bogged down in low value projects, and that you want to make a difference quickly. And, with slim budgets for new tools and consulting services due to the Economy, it’s a good approach for Data Quality and Data Governance today. Now which sagging branches are the most attractive?

Review Existing Processes

Have you reviewed your matching logic for external data entering your systems? What about the rejection rows from your ETL? These activities are essentially free - you can do them while you’re sitting on conference calls or waiting for others to join a meeting. They don’t take long but you may see patterns that help you to recommend great new projects.

Rethink Rollout of Underutilized Tools

I was at a conference recently and saw a demo of a Data Quality report from a vendor we work with. I went back and asked my Sales Rep if we owned the tool, and sure enough we do. It’s part of a larger contract, but no one is using it. Ca-Ching. That’s a free reporting tool from my perspective. How am I going to use it? To rollout Key Quality Indicators (KQIs) of course!

Educate, Communicate, and Build Relationships

Another freebee. A down economy is a great time to reach back out ot the business to understand their issues, and how you can help to resolve them. Also, take the time to formalize your message. Create a "walking deck" if you don’t have one. A walking deck is 3 or 4 slides you keep in your binder that you can present whenever the topic comes up. I use these when I meet someone new to quickly educate them on the Data Governance work at my company. It’s a relationship building opportunity that could lead to a new sponsor or commitment from a new department to join in your efforts.

Please comment with other ideas!