The use of blockchain distributed ledgers in business processes is now a common theme in many business software vendors’ presentations. The technology has a multitude of potential uses. However, presentations about the opportunities for digital transformation always leave me wondering: How is this magic going to happen? I wonder this because the details about how data flows from point A to point B via a blockchain are critically important to blockchain utility and therefore the pace of its adoption.
Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI, forecasting, consolidating
Ventana Research uses the term “predictive finance” to describe a forward-looking, action-oriented finance organization that places emphasis on advising its company rather than fulfilling the traditional roles of a transactions processor and reporter. Technology is driving the shift away from the traditional bean-counting role. The cumulative evolution of software advances will substantially reduce finance and accounting workloads by automating most of the mechanical, rote functions in accounting, data preparation and reporting. (I recently summarized these in a “Robotic Finance”)
Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI
This has been a dramatic year for Informatica, a major provider of data integration software. In August it was acquired and taken private by Permira funds and Canada Pension Plan Investment Board for about US$5.3 billion. This change was accompanied by shifts in its management. CEO Sohaib Abbasi became chairman and now has left, and many executives were replaced while Anil Chakravathy became CEO from being the Chief Product Officer. The new owners appear to have shifted the company’s strategic priorities to emphasize profitability with reduced headcount and return on the purchase investment. Despite these changes, during the past six months Informatica has made key product announcements that will impact its future and the future of data management.
Topics: Big Data, Data Quality, Master Data Management, MDM, Operational Performance Management (OPM), Cloud Computing, Data Integration, Data Management, Data Preparation, Governance, Risk & Compliance (GRC), Informatica, Information Management, Business Performance Management (BPM), Information Optimization, Risk & Compliance (GRC)
Organizations today create and collect data at ever faster rates, and this introduces challenges in ensuring that data is not just managed but used in a consistent manner for a range of operational and analytic tasks. This is made more difficult by new data sources whose definitions vary from standard and widely used formats. Making all information available and consistent is essential to support business processes and decision-making. A key technology tool for this effort is master data management (MDM). Every business area needs MDM, whether it deals with customers, products, employees, finance or others individually or collectively in what is called multidomain MDM. It is an essential tool for data governance across an organization, which has become a focal point for improvement as many organizations spend significant time in data-related tasks. Our benchmark research on information optimization shows that preparing data for analysis (47%) and reviewing data for quality and consistency issues (45%) are the two information tasks that consume the most time. Properly used MDM enables data stewards and other IT professionals to improve the consistency and quality of departmental and enterprise data.
Topics: Big Data, Data Quality, Master Data Management, Sales, Sales Performance, Social Media, Supply Chain Performance, Golden Records., MDM, Operational Performance, Analytics, Business Analytics, Business Performance, Cloud Computing, Customer & Contact Center, Data Management, Financial Performance, Information Applications, Information Management, Workforce Performance
When applying information technology to drive better business performance, companies and the systems integrators that assist them often underestimate the importance of organizing data management around processes. For example, companies that do not execute their quote-to-cash cycle as an end-to-end process often experience a related set of issues in their sales, marketing, operations, accounting and finance functions that stem from entering the same data into multiple systems. The inability to automate passing of data from one functional group to the next forces people to spend time re-entering data and leads to fragmented and disconnected data stores. The absence of a single authoritative data source also creates conflicts about whose numbers are “right.” Even when the actual figures recorded are identical, discrepancies can crop up because of issues in synchronization and data definition. Lacking an authoritative source, organizations may need to check for and resolve errors and inconsistencies between systems to ensure, for example, that what customers purchased was what they received and were billed for. The negative impact of this lack of automation is multiplied when transactions are complex or involve contracts for recurring services.
Topics: Big Data, Mobile, Sales Performance, Supply Chain Performance, ERP, Office of Finance, Operations, Management, close, closing, computing, end-to-end, Operational Performance, Analytics, Business Performance, Cloud Computing, Data Management, Information Applications, Information Management, CRM, Data, finance, FPM