Using information technology to make data useful is as old as the Information Age. The difference today is that the volume and variety of available data has grown enormously. Big data gets almost all of the attention, but there’s also cryptic data. Both are difficult to harness using basic tools and require new technology to help organizations glean actionable information from the large and chaotic mass of data. “Big data” refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially those related to human behavior and interaction. The challenges in dealing with big data include having the computational power that can scale to the processing requirements for the volumes involved; analytical tools to work with the large data sets; and governance necessary to manage the large data sets to ensure that the results of the analysis are accurate and meaningful. But that’s not all organizations have to deal with now. I’ve coined the term “cryptic data” to focus on a different, less well known sort of data challenge that many companies and individuals face.
Topics: Big Data, Data Science, Planning, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, FP&A, Human Capital, Marketing, Office of Finance, Operational Performance Management (OPM), Budgeting, Connotate, cryptic, equity research, Finance Analytics, Kofax, Statistics, Operational Performance, Analytics, Business Analytics, Business Performance, Financial Performance, Business Performance Management (BPM), Datawatch, Financial Performance Management (FPM), Kapow, Sales Performance Management (SPM)
Analytics has long been a core discipline of Finance, applied to analysis of balance sheets, income statements and cash-flow statements. However, as I’ve noted, most finance departments have not kept up with recent advances. Our recent research in finance analytics shows that few organizations are realizing the potential of more advanced analytic methods and tools such as predictive analytics and driver-based modeling. One reason for this sluggishness is that they have not looked past yesterday’s requirements to see what possible. Another is that they are distracted by the difficulties they face in simply doing tried-and-true analysis, which is the result of difficulties in accessing the necessary data and inadequate tools. A third reason is that people receive too little training in the application of analytics to business and the use of more advanced analytic tools and methods.
Our research consistently finds that data issues are a root cause of many problems encountered by modern corporations. One of the main causes of bad data is a lack of data stewardship – too often, nobody is responsible for taking care of data. Fixing inaccurate data is tedious, but creating IT environments that build quality into data is far from glamorous, so these sorts of projects are rarely demanded and funded. The magnitude of the problem grows with the company: Big companies have more data and bigger issues with it than midsize ones. But companies of all sizes ignore this at their peril: Data quality, which includes accuracy, timeliness, relevance and consistency, has a profound impact on the quality of work done, especially in analytics where the value of even brilliantly conceived models is degraded when the data that drives that model is inaccurate, inconsistent or not timely. That’s a key finding of our finance analytics benchmark research.
Topics: Big Data, Planning, Predictive Analytics, Governance, Office of Finance, Budgeting, close, Finance Analytics, Tax, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Financial Performance, Governance, Risk & Compliance (GRC), In-memory, Information Applications, CFO, Risk, CEO, Financial Performance Management, FPM
Business computing has undergone a quiet revolution over the past two decades. As a result of having added, one-by-one, applications that automate all sorts of business processes, organizations now collect data from a wider and deeper array of sources than ever before. Advances in the tools for analyzing and reporting the data from such systems have made it possible to assess financial performance, process quality, operational status, risk and even governance and compliance in every aspect of a business. Against this background, however, our recently released benchmark research finds that finance organizations are slow to make use of the broader range of data and apply advanced analytics to it.
Topics: Big Data, Planning, Predictive Analytics, Governance, Office of Finance, Budgeting, close, Finance Analytics, Tax, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Financial Performance, Governance, Risk & Compliance (GRC), In-memory, Information Management, CFO, Risk, CEO, Financial Performance Management, FPM
One of the most important IT trends over the past decade has been the proliferation of ever wider and deeper sets of information sources that businesses use to collect, track and analyze data. While structured numerical data remains the most common category, organizations are also learning to exploit semistructured data (text, for example) as well as more complex data types such as voice and image files. They use these analytics increasingly in every aspect of their business – to assess financial performance, process quality, operational status, risk and even governance and compliance. Properly applied, business analytics can deliver significant value by deepening insight, supporting better decision-making and providing alerts when situations require attention from managers or executives.
Topics: Planning, Predictive Analytics, Customer, Human Capital Management, Office of Finance, Budgeting, close, closing, Finance Analytics, PRO, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Performance, Cloud Computing, Financial Performance, CFO, Risk, costing, FPM, Profitability