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SmartData Collective > Business Intelligence > Decision Management > Transforming Fragmented Decision Making
Decision Management

Transforming Fragmented Decision Making

JulieHunt
JulieHunt
4 Min Read
Transforming Fragmented Decision Making
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
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Many midsized organizations are uncertain about how to proceed with business intelligence and analytics – or if they should even jump in. But if these organizations fail to include various data analytics as inputs to strategic and tactical decisions throughout the enterprise, they may be blocking their abilities to compete, grow and keep pace with dynamic market and economic changes. Key opportunities may be missed; detrimental risks may pop up without warning.

Many midsized organizations are uncertain about how to proceed with business intelligence and analytics – or if they should even jump in. But if these organizations fail to include various data analytics as inputs to strategic and tactical decisions throughout the enterprise, they may be blocking their abilities to compete, grow and keep pace with dynamic market and economic changes. Key opportunities may be missed; detrimental risks may pop up without warning.

Like larger enterprises, midsized organizations have a lot of data from a large variety of sources. Most decision-making calls for more timely data that can be used to point the way for the future, not just looking at what happened in the past. Too often spreadsheets become the key data source – unfortunately spreadsheets may not yield the necessary analysis, particularly when the situation calls for data from multiple disparate sources.                

More software solutions are available that should work well for midsized organizations and are more cost-effective, including cloud services that offer analytics anywhere, anytime. But many midsized organizations have real hurdles to implementing agile analytics and decision-making processes. They may not have enough IT resources or analytics experts to effectively go from data to insights, and then on to decisions and actions. Data isn’t the only resource that needs attention – business processes are data’s partner in analytics that ultimately engender business improvement. And just like larger enterprises, midsized organizations may not have the essential alignment between Business and IT, working together to make sure analytics make sense for the business needs of the organization.       

Companies can benefit greatly from analytics that support initiatives that impact the entire organization and reveal solid business value. For instance, to learn more about customers, organizations need to analyze: whether products and services meet customer needs, how customers want to interact with the company for different activities, and how different segments of customers prefer to make purchases. An activity like customer segmentation doesn’t just benefit marketing – it makes a significant difference for corporate customer strategies, innovation, product groups, and sales.

Centralizing analytics can eliminate redundant effort to glean intelligence from data sources, while reducing effort and cost. Often departments in midsized organizations rely too much on their own silos of data and don’t include a wide variety of data sources. Centralizing advanced analytics should dynamically work to eliminate data silos in the organization and bring different perspective into the formulation of analytical models and a wider breadth of benefit to the overall organization. Midsized companies will find the best success when all functions across the enterprise work in harmony.

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