Data and analytics can deliver profound benefits to midsize enterprises (MSEs), and are becoming critical for differentiation and even long-term survival.
MSE farming operations can drastically improve productivity, growth and margins by using analytics disruptively, according to Gartner. Farmers can increase their ability to farm exponentially higher acreage with the same number of employees (or fewer). Video analytics and visualisations enable retailers to understand shopper profiles and shopping traffic patterns.
MSE financial services providers are working continuously to improve their risk models because the more accurate their assessment of risk, the better their chances of profitability. MSE manufacturers worldwide are increasingly looking into the causes of quality fluctuations by combining “what if?” analysis with sensitivity analysis and predictive models.
To cope with the innovation and change impacts of these projects, leading MSEs are preparing for expanded data science capabilities by drawing on new and simplifying technologies. These include supporting citizen analysts to deploy and derive value from augmented analytics, one of four major data and analytics trends that MSE CIOs should be paying attention to.
“Trends in data and analytics technology, including the rise of machine learning and artificial intelligence, represent major developments that can’t be ignored,” says Alan Duncan, vice-president analyst at Gartner. “CIOs in midsize enterprises should embrace such trends as they strive to develop data-driven organisations that deliver new business value.”
Trend one: Deploy visual data discovery for better data-driven business decisions
Traditionally, midsize teams view data in a few discrete platforms. But over the past five years, technology has emerged that layers data from separate tools into a tightly coupled interactive visualisation layer. Visual data discovery enables CIOs to blend data quickly, which means that they are able to diagnose business problems and regularly test whether current operations are effective.
These platforms are mostly cloud-based, offering flexibility and scalability alongside deeper diagnostic analytical capability. “These solutions offer MSE CIOs the maximum opportunity to engage with, and support, the business user community,” says Duncan.
Trend two: Use data preparation tools for more productivity and data governance
Getting data ready to use can be time-consuming and difficult, and MSEs often lack the correct resources to do it well. Data preparation is an iterative, agile process that enables enhanced and streamlined data preparation efforts and improves data sharing, reuse and governance.
Data preparation tools give users the opportunity to see important connections within the data and share the findings. Emerging technologies such as augmented machine learning capabilities and data catalogues make it even easier to share business insights.
Trend three: Enable more self-service and automation with augmented analytics
Data visualisation simplifies some data and analytics challenges, but identifying insight and building the analytics models remains complex and time-consuming. Additionally, it’s difficult to know which insights to act on and which are significant. Augmented analytics uses artificial intelligence (AI) techniques to simplify analytical processes such as data preparation, insight discovery and insight sharing.
“This enables business users and citizen data scientists to automatically find, visualise and narrate relevant analytical findings, such as correlations, exceptions, clusters, segments, outliers and predictions, without having to build models or write algorithms,” says Duncan.
Trend four: Implement predictive analytics to optimise and embed analytics in high-value business scenarios
Predictive analytics answer the question, “What is likely to happen?” Previously, marketers have used the technology to figure out what customers were likely to do, but predictive analytics are now being embedded in more business applications than ever. As enterprises using the technology continue to report good results, and as the amount and quality of data increases, so has interest in this technology.
Predictive analytics are also relatively easy to deploy for specific business functions. MSEs can implement packaged applications for most use cases, although these options can be limited in agility, customisation and how much competitive differentiation they offer. The alternative is to task the IT team with improving its analytics skills and design thinking or partnering with an external data and analytics service provider.