If data is the new oil, then knowing how to refine it into actionable business insights is the key to unleashing its potential and may raise the profile of IT leaders who can harness analytics to make business more efficient or improve customer experiences.
Accordingly, many CIOs have turned their data science chiefs onto defining a data and analytics strategy, which has emerged as the top responsibility of 86 percent of data and analytics leaders, up from 64 percent in 2016, according to Gartner research published in October.
“IT leaders need to look at data first to succeed in their digital initiatives, rather than treating them as an afterthought to help with ad hoc projects,” says Gartner analyst Mike Rollings. He adds that 45 percent of more than 3,000 respondents to Gartner’s 2019 CIO survey said that they were boosting their investment in business intelligence and data analytics.
CIOs who applied analytics in the hopes of boosting business growth recently shared lessons learned and advice for peers undertaking similar efforts.
McKesson prescribes better data analysis
Health-care company McKesson is cleaning up petabytes of data under the stewardship of Chief Data and Analytics Officer Brian Dummann, who is consolidating several enterprise data warehouses into Snowflake, running in Google Cloud Platform (GCP).
“We were looking for a partner to help challenge us to go faster,” Dummann says of GCP, which offers machine learning capabilities that McKesson is leveraging to improve business operations and, eventually, to fuel new business models.
The $214 billion company is aiming to derive real-time business insights that help medical supply manufacturers and pharmaceutical companies improve patient outcomes, a major area of focus at a time when health-care providers are focused on value-based care. “The driver reason is about speed and agility for data and analytics to create value more rapidy — days or weeks rather than months,” Dummann says.
Moving to Snowflake on GCP will help data scientists and business analysts spend more time doing analytics rather than ingesting data. Moreover, the new platform will help McKesson evolve from descriptive to predictive and prescriptive analytics.
Lesson learned: “Cloud first” is the right path because even with solid data science teams, most companies can’t match the speed and scalability that cloud providers can offer for business insights. This agility will in turn help McKesson’s transformation toward becoming an “insights-driven culture,” Dummann says.
La-Z-Boy converts analytics into business value
Data informs virtually every decision at La-Z-Boy, which uses analytics to improve operations in 20 departments, including HR, finance, supply chain and sales, says Erika Janowicz, business intelligence and data manager at the international furniture retailer. Analytics software helps La-Z-Boy manage pricing, SKU performance, warranty, shipping and other information for more than 29 million variations of furniture and other products. It also helps forecast inventory levels.
The secret sauce behind the software, provided by Domo, are alerts the software sends when data is updated or when certain thresholds are triggered that require action by the custodian of the data, says Janowicz. As with most visualization tools, Domo renders La-Z-Boy’s data in an intuitive graphical dashboard that’s easy to grasp.
But it wasn’t always an easy sell, as the biggest change management hurdles included getting business staff to use the tool for the first time. And the tool isn’t for everyone. “Whenever I get a new team, first we have a conversation where I learn more about their needs and goals to make sure Domo is the right tool for them,” Janowicz says.
Lesson learned: The right application of data can make the difference between a green light or a no when it comes to securing money to pursue initiatives. “A couple of years ago, our CEO said, ‘If you want more money in your budget, prove it with data,'” Janowicz says.
Sales analytics take wing at Southwest
To help remove friction from business travel, the corporate sales department at Southwest Airlines (SWA) used logistic regression and behavior-based attributes to develop growth forecasts that help inform corporate sales teams about service offerings, contract compliance and new patterns in behavior, says Seth Quillin, senior manager of sales analytics at SWA.
Combining the output of 20 data models, as well as client information from SWA’s Salesforce.com system, SWA fashioned guides that help sales staff understand why accounts have bubbled up to their attention and how to influence clients.
Corporate sales learned more about their accounts, which helped them become trusted advisors to their clients. The software resulted in increased business trips and revenue. “One of our top priorities is serving corporate clients better and removing points of friction,” Quillin says.
Lesson learned: There’s no shame in getting help if you need it. SWA worked with consulting firm Elicit to build the models. “We brought in Elicit as an extra lens to help us bring better insights and personalization and customization,” Quillin says.
Accenture analytics facilitates sales, utilization and diversity
Analytics guides many of the decisions made at Accenture, says Andrew Wilson, the consultancy’s former CIO. For example, the professional services company’s Win Probability Tool leverages key metrics to score the likelihood of winning potential business opportunities.
The application churns through Accenture’s Salesforce.com CRM data, taking into account several years’ worth of deals, as well as geography, price points, margins and other metrics to predict loss potential with 90 percent accuracy.
At a time when enterprises place premiums on resource allocation, Accenture also uses analytics to track technology device and real estate utilization. The device dashboard displays utilization trends by location, with the ability to drill down at the device level. The space dashboard shows the use of seats and meeting rooms in Accenture offices and delivery centers drawing on nine data sources refreshed monthly.
Both apps enable leadership to make critical decisions that improve the experience for Accenture’s 500,000 employees, many of whom work remotely and travel a great deal. “Getting the right utilization in terms of space and technology is key,” Wilson says. “We have to be very careful of how we allocate fixed real estate and office space.”
Lesson learned: The bigger the enterprise, the more value that is trapped in the data it has collected. Accenture is constantly revisiting its approach to providing consulting services, which means evolving what data it curates. “A digital strategy has analytics at the core,” Wilson says.
Belkin charges up its analytics strategy
At Hon Hai–owned Belkin, CIO Lance Ralls is gearing up for analytics around customer and operational information. But before he can execute on that strategy for the maker of charging cables, adapters and cases for smartphones, laptops and other devices, Ralls must lay a strong data foundation.
So Ralls’ team is combing through a variety of Excel spreadsheets for data that will eventually be aggregated into a data lake. They are also exercising copy data management, a practice in which users can rapidly roll backward and forward through snapshots of financial and business reporting data to identify issues. The software, from Delphix, also enables Belkin to virtualize, compress and protect data.
“Users feel much more comfortable with the data and can run a lot more reports, giving the business more real-time data for analytics,” Ralls says.
Lesson learned: Prepare now for 5G. If CIOs aren’t already thinking about this ultra-fast cellular network technology, they should, says Ralls, who is already thinking about how the eventual “explosion of data” enabled by 5G will impact the wireless gateways, routers and other Belkin products that provide connectivity for consumers.
Top takeaway: Data and analytics departments must flip from servicing a list of projects from the periphery to grounding an enterprise-wide approach that informs how internal and external data can be used to deliver business value. “There is no more room for a strategy specific to the data and analytics team, but instead an imperative for an enterprise-level operating model,” says Gartner’s Rollings.