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Data Analysis – It Takes a Team

For many companies, institutionalizing analytics to mine the torrential amount of data streaming into their networks can create myriad challenges.

team Data Analysis – It Takes a TeamWhile the shortage of data scientists has garnered a lot of attention, many companies are overlooking two critical things – the need to identify the data analysis roles they really need and building “customer service” mentalities into their efforts to glean actionable insight from data.

There are five key roles companies should consider when staffing their data analysis teams, according to a blog post by McKinsey & Co.:

1. Data Hygienists make sure that data coming into the system is clean and accurate, and stays that way over the entire data lifecycle. Hygienists would tackle questions such as: Are the time values being captured the same? Or, have old data fields been populated with new types of data but under the old field names?“

“If this is not addressed in the database, new product data may override old product data, rendering a meaningless result,” according to McKinsey. “This data cleaning starts at the very beginning when data is first captured and involves all team members who touch the data at any point.”

2. Data Explorers identify data that can be meaningful to the organization. “That can be a significant task because so much data out there was never intended for analytic use and, therefore, is not stored or organized in a way that’s easy to access,” the post notes. “Cash register data is a perfect example. Its original function was to allow companies to track revenue not to predict what product a given customer would buy next.”

3. Business Solution Architects link the discovered data together and organize it so that it’s ready to analyze.

4. Data Scientists take the data organized by the architects and create sophisticated analytics models that can be used to help predict customer behavior and allow advanced customer segmentation and pricing optimization.

5. Campaign Experts turn the models into insight. “They have a thorough knowledge of the technical systems that deliver specific marketing campaigns, such as which customer should get what message when,” according to McKinsey.

“They use what they learn from the models to prioritize channels and sequence the campaigns – for example, based on analysis of an identified segment’s historical behavior it will be most effective to first send an email then follow it up 48 hours later with a direct mail,” the post notes.

Once the required team members are assembled, they need to show stakeholders how data analysis can help the company.

That requires thinking of the business owners as customers. To ensure adoption of a service bureau culture, measure personal performance by business success, not just volume or speed as too often happens,” McKinsey notes. “Some companies have developed bonus criteria for members of their big data teams based on how quickly and broadly a model was adopted by the internal customers rather than how innovative the model was.”

Once the team has been put into place and has started to demonstrate its value, it’s important to develop an analytics prioritization plan that outlines the process for handling competing stakeholder demands without taxing the resources of the team.

Companies should start by developing frameworks to track requests being submitted and subsequently thorough the lifecycles of the analytics projects, according to a recent Search Engine Watch blog post.

Organizations then need to develop criteria for weighing requests; many will seek to attach a monetary value to requests.

“Most leaders in the industry will look for a strategic value assessment, which is a KPI involving equal parts revenue generation potential, estimated cost savings, and increased customer satisfaction,” according to the post.

Once established, the prioritization model should be reevaluated throughout the year.

“For instance, if the strategic alignment of the insights team is driving sales rather than reducing customer churn, there should be less overall prioritization granted to requests that are related to operational issues and customer experience,” according to Search Engine Watch.

That doesn’t mean organizations should ignore critical issues.

“But if at the end of the year the insights practice delivered 80 percent of their recommendations for customer retention questions, they may be misaligned and under-delivering with respect to their sales focus,” the post notes.

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