Your Analytics Program is Not Ready for Machine Learning
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Your Analytics Program is Not Ready for Machine Learning

Your Analytics Program is Not Ready for Machine Learning

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If you are like most organizations, your analytics program is not ready for advanced analytics technologies such as machine learning. Here are a few steps to take to get you closer, or a way you can leapfrog the work to reap the benefits now.

 Machine learning and AI are capturing all the attention right now, but is your organization really ready to dive into using these technologies? Probably not.

A new Gartner maturity model for data and analytics and a survey of or 196 organizations found that most organizations’ existing analytics programs probably aren’t mature enough to be able to support such advanced analytics capabilities.

“Fundamentally machine learning is built on not only having the right skills, but the right processes, and the right data in place to be able to feed these machine learning models, Gartner research VP Jim Hare told InformationWeek in an interview.

At the top level of the model, mature data and analytics programs are those where data and analytics are central to the organization and aligned with its goals and business strategy and investments. In these organizations, the chief data officer sits on the board.

But if you aren’t there yet, you are not alone. Only about 9% of organizations fit this profile, even though CIOs have said it’s a top investment priority.

Most organizations responding to the survey said they were more in the middle levels of the maturity model. Most respondents assessed themselves at level three (34%) or level four (31%), while 21% said they were at level two, and just 5% said they were at the most basic level, level one.

Level one is pretty basic, indeed. Gartner’s model says those at level one do not exploit data. Their data is managed in silos and people argue about whose data is correct. Analysis is ad hoc and relies on spreadsheets and “information firefighting.”

As organizations move up in the maturity model, data becomes more central to the organization, leaders become data champions, and the analytics program becomes more closely aligned with the business. You can see the full maturity model diagram in this Gartner press release.

So how do you get to a higher level, and what’s holding you back? Gartner said that organizations reported a broad range of barriers and the top three were defining the data and analytics strategy, determining how to get value from projects, and solving risk and governance issues.

Hare told me that to improve their maturity models organizations need to work on improving each of four different maturity components — strategy, people, governance, and technologies. If they find themselves hitting a wall on their program, it’s likely one of these things is holding back their progress. He has three recommendations for organizations trying to level up in the maturity model.

1. Start at the top

“It involves building the right culture, and it really starts at the C-level,” Hare said. Grassroots efforts will only have a limited impact because the culture doesn’t permeate across the entire organization in the same way. “To move to the next level and create a data-driven culture, it really needs to start at the top.”

2. Define performance measures

Hare said that organizations should identify the hard benefits of these programs. This is where the IT and business organizations need to come together.

“You need to evangelize the hard benefits these projects can provide in business terms,” he said. “You have to be marketing what the value of these projects are. No one else will do that on your behalf.”

As part of this step, you also shouldn’t be afraid to pull the plug if you find the program going down the wrong path, Hare said.

3. Centers of excellence

Hare’s third recommendation is to create an Analytics Center of Excellence to foster collaboration among the right constituents, including those in charge of enterprise architecture and the data scientists. This center should factor in everything that’s needed, from enterprise architecture to data requirements.

That said, there is a shortcut to using advanced analytics technologies, Hare said. You can buy it. Organizations who are struggling can leapfrog ahead by buying packaged software that incorporates machine learning and other advanced analytics. Hare said that technology vendors, CRM and ERP vendors, for instance, are adding machine learning capabilities into their products. Of course, these won’t be customized to your business out of the box. But some do have the capability to allow for some degree of customization.

 

Source by : Information Week

Author by : Jessica Davis .

She has spent a career covering the intersection of business and technology at titles including IDG’s Infoworld, Ziff Davis Enterprise’s eWeek and Channel Insider, and Penton Technology’s MSPmentor. She’s passionate about the practical use of business intelligence, … View Full Bio

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