Collecting data is no longer a serious challenge for businesses, but analyzing data is still a battle. Not only does technology make it possible to collect and store data, but it can be used to draw out insights and make data-driven decisions using predictive analytics. Businesses that previously didn’t have to rely on data, information technology, and analytics are discovering that this new method of producing predictive insights can help boost efficiency and profitability.
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Defining Predictive Analytics
Predictive analytics employs historical and real-time data mining and other techniques to predict outcomes and inform decision-making for businesses and other organizations.
Industry Statistics and Market Predictions
Various studies indicate a substantial projected upswing in the predictive analytics market. Research by MarketsandMarkets projects a growth from $4.56 billion in 2017 to $12.41 billion in 2022, a compound annual growth rate (CAGR) of 22.1%. The International Data Corporation (IDC) predicts that big data and business analytics’ worldwide revenues will grow from $130.1 billion in 2016 to $203 billion in 2020, a CAGR of 11.7%. Meanwhile, the McKinsey Global Institute’s 2016 report, The Age of Analytics: Competing in a Data-Driven World, predicts that 2 million to 4 million business translators will be needed to translate info gathered by data scientists over the next ten years.
This projected growth tends to focus on a specific methodology. According to the 2017 Advanced and Predictive Analytics Market Study conducted by Dresner Advisory Services, more than 80% of enterprises interviewed consider in-memory analytics and in-database analytics the most critical predictive analytics platforms.
Exploring the Possibilities
Several industries stand poised to reap the benefits of predictive analytics. Manufacturing companies can use it for inventory and resource management and for optimizing production and distribution. Health insurance can use it to discover fraudulent claims, identify at-risk patients, and finding effective interventions for them once they’re discovered. Predictive analytics can also help government and public sector entities enhance their cybersecurity and improve their understanding on population trends. The retail industry could use predictive analytics to boost marketing and promotional event effectiveness, which could include an improved determination on which products to feature. The oil and gas industries could also use predictive analytics to minimize safety risks and to predict equipment maintenance. Finally, banking can benefit from predictive analytics by improving their ability to detect and reduce fraud and credit risk while simultaneously maximizing various upsell and cross-sell opportunities.
Indicators of Potential to Benefit from Predictive Analytics
Businesses will benefit from predictive analytics if they have certain parameters currently in place. For instance, they’ll benefit if their performance needs depend on improving prediction accuracy. They’ll also get a boost if they carry a potential for large value from merging data from multiple sources. Businesses that heavily rely on research and development would find predictive analytics essential, as would businesses that have underutilized assets due to inefficient signaling. Businesses that depend on personalized data, have a mismatch of supply and demand, and carry fragmented data could also use predictive analytics to their beneficial advantage.
That said, studies indicate that businesses only capture a portion of potential value from predictive analytics. According to the 2016 McKinsey Global Institute report The Age of Analytics: Competing in a Data-Driven World, only 50% to 60% of potential value is captured in the location-based data industry. This percentage dips to 30% to 40% in the retail and health care industries and drops to 20% to 30% in the manufacturing industry.
Barriers to Adopting Predictive Analytics
Older generations of executives and leaders are hesitant to trust in data and analytics for decision-making. According to a KPMG and Forrester Consulting survey of more than 2,000 decision-makers from 10 countries, just 34% trust in data and analytics for insights into business operations. Only 38% have confidence in customer insights from data and analytics, and a mere 43% believe data and analytics are useful for risk and security.
There are other barriers beyond generational roles. These include high implementation costs, difficulty in collecting the proper data, challenges associated with converting info into actionable plans, a lack of data-deciphering talent, and the sheer complexity of the concept.
How it Works with Predictive Analytics
General Electric (GE)
The appliance giant uses predictive analytics to gather data from sensors installed on gas turbines and jet engines. This data is analyzed via data reports to develop tools that increase efficiency. GE predicts this could lead to a 1.5% boost in productivity over a 20-year period.
The famed coffee company uses predictive analytics to choose locations for its stores, gathering data on key metrics like location, traffic, demographics, and customer behavior. The data is analyzed to predict a potential spot’s success rate and revenue growth.
Ralph Lauren, Lucky Brand, and True Religion
These companies use the platform Makersights to make product design and development decisions. The platform allows customers to give short, structured feedback and insight on brands. The retailers apply sales data and machine learning to this feedback to minimize markdowns, take advantage of top-selling products, inform product design, and increase gross margins.
Predictive analytics brings deep insights to the way companies operate. Many aspects of business like finding and retaining customers, streamlining manufacturing, and avoiding risk and loss, can benefit from the analysis of data. As the amount of available data grows, the businesses that are able to translate into useful, strategic insights will thrive.