While companies have high expectations about the role of analytics, there is still significant work ahead to operationalize analytics models and maximize the benefit of data-driven decision-making.
Companies aim to flex their analytics muscle on a variety of business challenges, such as improving customer experience and engagement, optimizing enterprise productivity, and building more innovative products. Yet in order to reap sustainable business benefits with bottom-line impact, companies need to address analytics as a holistic operational strategy, not just as a series of one-off projects.
Most companies have yet to come up with a mature plan for operationalizing analytics. Too many firms are allocating significant dollars and sizeable resources to building analytics models that never deliver on their expected value, which only serves to waste time and money. According to SAS research, less than half of the best models get deployed while 90% of models take more than three months to deploy. In the study, almost half (44%) of models take even longer—more than seven months before they reach production.
Deriving advantage from data-driven decision-making is only possible if companies are willing to do the work to operationalize analytics. “Positioning analytics in the core of a company’s operation is a strategic mission,” notes Tony Flath (@TmanSpeaks), president of TmanSpeaks LTD. “With the introduction of artificial intelligence models, we can automate analytics to set up solutions in all areas of the company to drive operational efficiency, consumer insights, innovation, and customer experience.”
Given that employee time, energy, and enthusiasm are precious, organizations have to adjust strategies to prioritize and position analytics so it can have significant impact across all areas of the business. “Smarter and faster organizations leverage data, analytics, forecasts, and other information to make and adjust plans based on volatile working environments and changing market conditions,” notes Issac Sacolick (@nyike), president of StarCIO and author of Driving Digital. “The main goal of operationalizing analytics is to democratize it, providing employees with a wide lens to the world and aligning the organization to business opportunities.”
Getting Closer to Customers
Many companies are prioritizing analytics to tailor products and services and to elevate customer experience. Benjamin A. Martins (@Benni_aji), data analytics lead at SHIFT Nigeria, says his firm is leveraging analytics to help deliver “people-oriented services.”
Moin Shaikh (@moingshaikh), senior web analyst at Intelligent Online Solutions Pty. Ltd., contends analytics are critical to helping his organization understand the customer journey across multiple platforms while improving retention rates and repeat purchases for retail e-commerce transactions.
“We believe that understanding why we do what we do, what prospects and customers want from us, and defining what success looks like helps us operationalize analytics more effectively,” adds Gene De Libero (@GeneDeLibero), chief strategy officer and head of consulting at GeekHive.
Combined with playbooks for remediation, analytics can be operationalized into security practices to help organizations be more proactive about reducing cyber risks. With analytics, organization can more easily detect and deter adversarial threat actors, including ransomware attacks like data exfiltration and auction-powered extortion, far faster than through traditional manual processes.
“Being able to rapidly detect and evict threats is necessary in the modern enterprise to avoid regulatory and legal penalties while protecting confidential data or trade secrets,” says Kayne McGladrey (@kaynemcgladrey), a cybersecurity strategist at Ascent Solutions.
Analytics have also been instrumental in helping IT organizations create secure environments to support a remote workforce as companies recalibrated almost overnight for the COVID-19 era. At APS Marketing, for example, analytics are being operationalized into the enterprise security framework to help detect phishing exploits, unified communication fraud, and other threat vectors as the vast majority of staffers now work from home, according to Adam Stein (@apstein2), principal of the firm.
Challenges Lie Ahead
While companies have clarity about their business goals for data-driven insights, they are not as schooled in what it takes to operationalize analytics to ensure they can achieve those goals. In addition, data governance—specifically, how to cleanse and normalize data so it is of the highest quality and adheres to privacy and security standards—is an area that many companies struggle to formalize effectively. At the same time, companies can’t and shouldn’t write a blank check for analytics—they need to weigh the cost of operations versus the value analytics can provide to an organization, contends Arsalan Khan (@ArsalanAKhan), an advisor and blogger.
At Berkeley Varitronics Systems, analytics have proven to be a valuable asset in helping the firm streamline operations and, in turn, accelerate timelines for conceptualizing and delivering innovative products. “With so much analytical data out there to be perused and utilized, it’s important to determine the data set(s) providing the most bang for the buck,” adds Scott Schober (@ScottBVS), president and CEO. “[Analytics] allows us to deliver quality products on-time, which tops our list to maintaining revenue growth.”
For many companies, analytics remain somewhat of a mystical art form—a magic box that delivers insights and business value without a clear understanding of the work required. For a long time, technology journalist Jeff Cutler (@JeffCutler) held a counter view, believing analytics were less a driver of transformation and more a vehicle for presenting business data in charts and graphics—a technology without a “soul,” he contends.
That position shifted as analytics capabilities evolved and operationalization strategies allowed insights to directly drive business processes and change. “I’ve come around to understand there’s so much more to using analytics to build and grow a business,” Cutler says, adding that analytics have already helped his firm better understand data storage measures, maintain a social presence, and optimize resource allocation. Now the analytics focus is on improving customer engagement and productivity. “It’s amazing that I’m still in business 28 years later when it took me so long to embrace the ‘soul’ of analytics.”
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