Tag Archives: Data Management
Industry Week: Too Often, the Right Data Isn’t Getting to the Right Person (Even within Their Own Team)
“Closing the loop means establishing a system where the data is embedded into a continuous feedback process across the formerly siloed teams. Establishing this seamless data continuity fosters a data-driven culture where employees make decisions on a common set of accurate and up-to-date information.”
“A finance data model or enterprise information model transforms a generic ERP package into a specific structure to fit an organization’s business needs. Ideally, such a model would be tailored to the organization’s business activities and priorities while still allowing flexibility for new product launches, reorganizations, or acquisitions. A complex, layered, and well-defined enterprise information model can provide the business with flexible reporting, harmonized transaction processing, business model agility, and expanded views of transactions and profitability.”
“But old dogs can’t always learn new tricks. While Excel has shown remarkable staying power, some experts believe it may now be challenged beyond its reach. Companies big and small increasingly need to automatically pull their financial data from multiple cloud-based systems and utilize more advanced data analytics. Excel may be a roadblock to that…While Excel is often used as a collection point for data from other systems, it is still a “manual and largely siloed vehicle”…Excel’s limited ability to handle massive data sets “can lead to long processing times and more steps than other database tools.””
Here’s an important question to ask yourself when thinking about data:
What insights do you need to run the business?
In other words, what questions do you need answered, and which metrics would help answer those questions? This may involve financial results or nonfinancial information related to employees, customers, products, and market conditions.
- Which available data management tools might help? Being able to combine data from multiple sources and getting it to refresh automatically at the right frequency to meet the business need is the ultimate goal. But in the near term, see what you can gather easily through advanced data management tools like the one at https://edge.gg or even manually. Start with no more than 10 business questions so you can create visualizations of important results and explore relationships across data points. Once you begin automating your data, you can layer in more components to flesh out the picture.
- Is the leadership team aligned? All key parties need to agree on what will be measured, how it will be defined, who owns it, who will be accountable for producing it, and the business mandate being addressed. At heavily matrixed companies, getting everyone on board is no easy feat, but taking the time to do this upfront is crucial.
- Have you identified—and involved—your data ecosystem? As the company reaches certain milestones in, say, enabling automated data feeds with data quality controls or acquiring new tools for insight-driven decision-making, take the opportunity to test concepts in one market or line of business, create a prototype, and socialize your idea to gauge support. Be sure to involve those who will be using the new capability you plan to introduce.
- Is the workforce suitably equipped? A data ecosystem based on next-generation digital technologies can demand new or enhanced workforce skills and capabilities, such as storytelling with data, problem-solving using advanced analytics, and business partnering. Consider ways to build or acquire the talent you may require. Employees need a frictionless way to tap into the data flow, understand how to use it, and then act on it.
There are three key elements that should be in place before kicking off a data analytics project – understand the real need, understand the users, and understand the problem complexity.
“It has never been more urgent for businesses to adopt data analytics. Insights from data analytics are required to surpass, or, in some instances, merely remain on par with, competitors. Companies leveraging data analytics in response to the pandemic have already progressed along the path to digitization by establishing a data ecosystem—the infrastructure, applications, and analytics needed to drive business intelligence, generate insights, and inform strategic decision making.”
“Without quality-assuring governance, companies not only miss out on data-driven opportunities; they waste resources. Data processing and cleanup can consume more than half of an analytics team’s time, including that of highly paid data scientists, which limits scalability and frustrates employees. Indeed, the productivity of employees across the organization can suffer: respondents to our 2019 Global Data Transformation Survey reported that an average of 30 percent of their total enterprise time was spent on non-value-added tasks because of poor data quality and availability.”