Part 5 of the series, “Top Trends Impacting Midsize Businesses in the 2020s”
Confronted with significant disruption recently, most business owners are beginning to reassess how they can better prepare themselves as well as their workforce, supply chain, and customer-related operations. Whether they want predictive insights, automated processes, or greater visibility, all signs point toward a new data analytics strategy.
In fact, according to IDC, 40% of midsize companies are expected to adopt advanced analytics by 2023. These technologies include robotic process automation and artificial intelligence (AI). But perhaps more interesting is the finding Shari Lava, research director of small and medium business at IDC, shared during “Winning in the 2020s: Six Trends Every Midsize Business Needs to Know” – these investments are increasing by 10% annually, which is double the growth rate of overall IT spend.
Midsize businesses are unquestionably placing big bets on advanced analytics. IDC research pinpoints initiatives for offsetting the widening skill gap and responding to workforce demands for more strategic work as top reasons. But at the same time, companies are finding that the value of the real-time insight and scalability that these technologies support is undeniable.
Looking beyond the numbers for a differentiating edge
Midsize companies that are using their data and intelligence to run advanced analytics are creating unprecedented value for themselves and their customers. By tapping into real-time insight, they understand their potential impact on the marketplace and can scale their business model and operations as needed.
These benefits transcend the entire value chain – from product design, development, and delivery to aftermarket services, marketing communications and promotions, and sales engagements. However, they cannot happen without a solid foundation for data management and governance that addresses four critical necessities.
1. Bring order to a traditionally chaotic data reality
As a first step, companies should connect all data sources. Doing so helps organize and correlate the sprawl of data into a coherent information strategy. Then, the collection and correlation of metadata can be automated with the assistance of auto-discovery and statistically validated machine learning.
With this automated approach, organizations can scale the future collection and connection of information from various data sources. For example, a business technology platform can provide a range of unified connectors to most modern and traditional data sources that are inoperable with existing digital investments.
2. Manage data sprawl across all business functions
When handling a wide variety of data sources scattered across the business, consolidation is often the first instinct. But it is equally important to consider implementing a platform and prioritize cloud technologies.
Recent advancements in platform and cloud technologies have simplified implementation and improved time to market. Cloud solutions can now support multiple technology services and bundle them into a single solution. Meanwhile, various platforms can be connected and operate together as one synchronized ecosystem.
3. Lock in customer value with data privacy and security
Personally identifiable information (PII) has become a necessity when using advanced analytics. However, the collection, processing, use, and storage of this information must always be secure and appropriate.
For this reason, PII identification, masking, and auditability should be established – whether the data resides on premises, in the cloud, or across both environments. Additionally, all data access points must be assessed and monitored to mitigate emerging risks and attacks and further strengthen data protection – which are all fundamental for AI-enabled process automation and decision-making.
4. Move the data mindset from tactical to real business value
Management-level visibility into data value and risk empowers midsize companies to move from a traditionally technical approach for data governance to a strategy focused on delivering outcomes. Management can then prioritize data governance projects based on potential value creation, make decisions on data assets as part of a more extensive investment portfolio, and improve digital capabilities as business requirements shift.
A data governance strategy can be extended across the organization as widely as information is used. Every manager and employee – from customer service and fulfillment warehouses to marketing directors and financial analysts – can acquire and contribute knowledge and expertise. Once data becomes more reliable and trusted, the business is ready for AI-enabled, real-time, automated technologies.
Setting the stage for an intelligently advanced business future
The use of advanced analytics is undoubtedly a game-changer for all businesses. But all too often, increasing volumes of information from unwieldy, disparate sources get in the way, devouring much-needed resources and undermining the accuracy and reliability of insights.
The key to optimizing the value of advanced analytics is a foundation for consistent data management and governance, eliminating any hint of process complexity and insight confusion. And the midsize businesses taking up this mantle are the ones taking swift, bold, confident actions that keep every competitor guessing what’s next.
Discover how midsize companies are creating and enforcing contingency plans to get ahead of disruption successfully. Listen to an excerpt from the webinar “Winning in the 2020s: Six Trends Every Midsize Business Needs to Know,” with Timo Elliott, global innovation evangelist from SAP, and guest speaker Shari Lava, research director of small and medium business at IDC.
