But why stop complete picture there? To get a complete and accurate picture of your data without blind spots, you must deploy predictive models that score your data against intended outcomes. And use decision optimization that tells you, at the point of impact, what to do.
Planning in Analytics: What is our plan?
The process of financial complete picture performance management, “planning” for short, is essential to any large organization. Financial and business managers create plans, budgets and forecasts, perform analyses and create scenarios to assess the risks and rewards of alternative strategies and actions.
But for many, planning is a Herculean complete picture challenge and a major headache. Finance teams spend too much time on manual processes, collecting, consolidating and validating data before they can even begin to analyze it. In addition, finance professionals and managers often view budgeting, which is a fundamental part of the planning process, as onerous and of little value in managing operations or executing corporate strategy.
At the heart of these fragmented processes are “hidden” technologies, in many cases simply emails and spreadsheets.
Spreadsheets are ubiquitous
They’re appealing because of their familiarity, but they’re ill-suited to enterprise-scale planning and notoriously error-prone. One wrong keystroke and serious problems can spread! The most carefully crafted budget or forecast becomes a trap. Is that an exaggeration? Not really.
A spreadsheet error in your organization may never make headlines. But even small errors can cause headaches, embarrassment, and career problems. Truth be told, it’s not really the spreadsheet’s or the employee’s fault. It’s simply part of the nature of any process that involves many steps involving copying, pasting, and manually entering data.
That’s why leading organizations are supplementing their spreadsheets with new technologies to augment and automate their critical planning processes. Companies can now eliminate unproductive activities like tracking numbers, fixing broken links, and debugging macros. Instead, they can spend their time using best practices like direction-based planning and continuous forecasting to help them anticipate and respond to the disruptive forces driving the market.
Descriptive analysis: find out what happened.
“Analytics for everyone!” was the rallying cry that sparked a proliferation of desktop business intelligence (BI) tools across companies of all sizes and industries. Today, most companies feel they can answer “what happened” and are getting to the “why it happened” with BI, visualization, and integrated data science software.
Make no mistake: Yes, desktop country wise email marketing list tools have made self-service BI possible. These tools are producing beautiful charts and dashboards. They’re so compelling that many people are quick to act on what they see. While speed is good, acting before answering the most complex questions can have serious consequences.
To build real confidence in your data, start by fixing the issues that could hurt your analysis. Be aware of existing blind spots so you can target them before they hit you.
Blind Spot: Attractive Influencers
Attractive influencers aren’t just attractive because they present beautifully visualized answers. They also seem how to combine photos on an android device confident, seem certain of what they’re telling you, and may even echo your theory of how to move forward. They’ll tell you what you want to hear, not necessarily what you need to hear.
Blind Spot: Inconsequential Influencers
Gone are the days when clean emai reports sat on data scientists’ desks. With self-service desktop BI applications, anyone can produce reports. However, this has also created a number of inconsequential influencers.
Imagine a scenario where each influencer can choose different data sources. They may aggregate or analyze that data with different methods. If they miss a key factor or water down the meaning to produce a visualization, you are forced to base your decisions on unreliable sources. Even if the data shown is correct, the way the data is portrayed in the visualization may not be.
Treat your data intelligently.
Your data can be a phenomenal resource, but only if you treat it intelligently. Make sure you’re building your analytics on a solid foundation, otherwise the entire strategy will come crashing down.