Over just the past few years, technology has created a massive shift in how finance organizations operate and how they’re expected to perform. For more than 20 years Excel has led the field as a business finance tool, but now alternative tools such as Google Spreadsheets and a variety of BI software platforms have matured and begun to edge into Excel’s business. At the same time, new financial modeling, database, and analytics software has entered the market to provide deeper, real-time capabilities that help companies optimize every aspect of their business.
According to an April 2015 survey, fewer than half of finance leaders are satisfied with their organization’s budgeting methods. This will come as no surprise to anyone who has been involved in the lengthy, sometimes mind-numbing process of assembling and re-keying data in order to create a budget document that may simply be set aside as soon as it’s complete. Some organizations require as much as eight months to complete the budgeting process, at which point all of the painstakingly collected data is eight months obsolete.
Nobody intends to create a cumbersome labyrinth of financial modeling systems, yet it happens to even the best of organizations. As the business adds new departments, software, and leadership, financial models are pasted together to meet growing needs. Each team uses data from ERP, HR, General Ledger, and other systems in order to create reports and make decisions. Unfortunately, the larger the organization grows, the more disconnected the models can become.
With big data and analytics becoming such an important strategic initiative for businesses, the margin of error for project planning and execution can’t be taken for granted. There are a lot of critical steps involved to ensure you are getting the most out of your analytics investment. You need to have a dependable plan to establish key business requirements, reaffirm business goals, and match your investment priorities with the overarching business strategy.
With the advent of Hadoop, the question of whether it would take over certain data warehousing functions or replace data warehouses altogether has become a hot-button topic of discussion. Finding an answer to this question really depends on how a data warehouse is viewed by individual organizations.
If I were to tell you that companies around the world are prioritizing structured data analytics initiatives, you wouldn’t think twice about it. Given the progressive climate of big data and analytics, it seems like that would be a fair statement to make. Especially as many businesses continue to count on their relational databases, ERPs, CRMs, and other data management systems to organize, structure, store, and define their structured data sets to run more meaningful analytics.
Topics: Big Data Analytics
One of the most important lessons we are learning in the Big Data and Analytics Age is that simply having access to innovative technology isn’t enough to improve business outcomes. Technology needs the proper human input and interaction to elicit the types of results business leaders are expecting. Technology shouldn’t be counted on as a magic wand that will always deliver upon request.
Topics: Predictive Analytics
IBM Watson has been getting a lot of publicity for helping organizations tackle real-world issues on a national and global scale.
