DUBLIN, Calif.--(BUSINESS WIRE)--Sybase, Inc., an SAP company (NYSE: SAP) and industry leader in enterprise and mobile software, today issued the third and final installment of results from a new study – ‘Measuring the Business Impacts of Effective Data’ – an extensive study benchmarking some of the world’s leading Fortune 1000 companies across a wide range of vertical industries by measuring the direct correlation between a company’s IT investments and overall business performance.
The final installment of the study – ‘Impacts of Effective Data on Operational Efficiency’ – illustrates the impacts of effective data on operational efficiency, exploring how data impacts a company’s asset utilization, planning and forecasting accuracy, and on-time delivery of products and services— all leading indicators of supply chain performance, revenue growth and profitability. The study found that by better harnessing effective data, the median Fortune 1000 company surveyed stands to reap more than $270 million in savings from improved utilization of existing assets, and in some cases up to $2.6 billion, and that the accuracy of plans and forecasts can be increased by more than 18 percent.
“Fortune 1000 companies understand that improvements in operational efficiencies provide valuable returns, and this third installment of findings illustrates that small, incremental improvements in data effectiveness can provide substantial savings,” said Anitesh Barua, distinguished teaching professor and lead researcher, University of Texas at Austin. “In our previous two installments, we’ve shown direct links between data attributes and a company’s financial performance, and demonstrated how better data affects financial performance through superior innovation and growth. This final installment complements the previous two by establishing that superior financial performance from better data is also attributable to a corresponding improvement in operational performance.”
Five attributes of data effectiveness were considered, including quality, intelligence, usability, remote accessibility and sales mobility. According to the research, an improvement in just three of the five data attributes used in the study—quality, sales mobility and intelligence—can have a dramatic impact on operational efficiency:
- A median savings of $271 million and up to $2.6 Billion from improved asset utilization – The study found that improving the mobility of a salesforce so that it can transact with and exchange information directly with customers can positively influence asset utilization. An increase of 10% in salesforce mobility will lead to a 7.28% increase in asset utilization. Applied to the median organization in the sample with $4 billion in total assets, the equivalent reduction in total assets would be $271 million annually. For capital-intensive industries such as petroleum, machine tools, automobile and air transportation, the effect can be dramatic, with a slight improvement in mobility of data resulting in up to $2.6 billion in reduced assets.
- Increased planning and forecasting accuracy by 18.5% – Planning and forecasting accuracy is significantly impacted by intelligence of business data. An increase of 10% in business intelligence will lead to 18.5% increase in planning and forecasting accuracy. The median forecasting accuracy for the firms in our sample is 71.4%. A 10% increase in the intelligence of business data will revise this upward to 85%. A company that currently expects every 14 out of 20 forecasts to be pin-point accurate could expect an increase of 17 out of 20 forecasts to be accurate based on this data.
- Improved on-time delivery of products and services – Information quality impacts whether products and services are delivered on time. An increase of 10% in the quality of a company’s data will lead to 2.8% increase in the timeliness of delivery of products and services. The median value of timely delivery for the firms in our sample is 71.4%. A 10% increase in the intelligence of business data will revise this upward to 85%.
- Impact on vertical industries – Marked improvement in asset utilization in the retail and wholesale industries saw a 28% increase in asset utilization with just a 10% increase in sales mobility; while with the same 10% increase in sales mobility, the petroleum, machine tools, automobile and air travel industries saw maximum reduction in assets that are estimated in the range from $2 billion to $2.6 billion annually based on the study.
“The most rewarding aspect of working with our customers is helping them understand how to best leverage their existing data to uncover new and exciting revenue opportunities,” said Dr. Raj Nathan, executive vice president & CMO, Sybase. “The common theme that flows through each of the three installments is that improving data quality does not have to be a disruptive and costly undertaking. Just by taking incremental steps to improving data quality, organizations can enjoy significant returns in a relatively short amount of time.”
- Click here to download a free copy of this third installment on ‘Impacts of Effective Data on Operational Efficiency.’
- Click here to download a free copy of this second installment on ‘Impacts of Effective Data on Business Innovation and Growth.’
- Click here to download a free copy of the first installment of the report on ‘Financial Impacts of Effective Data.’
About the Study
The referenced study was commissioned by Sybase and conducted by the McCombs School of Business at the University of Texas, in conjunction with the Indian School of Business. The methodology of the study involved three steps – 1) operationalization of data attributes, survey design and testing, 2) data collection, and 3) analysis. The objective was to collect data from a variety of industries and functions. The questionnaire was tested for clarity and face validity and refined with the help of inputs from Fortune 1000 employees in functions who need accurate and timely information to make decisions or take actions (e.g., sales, forecasting, etc.). The final survey instrument was completed by over 150 respondents from Fortune 1000 firms. Financial and some operational performance data on the firms represented by the survey respondents was collected from archived sources. The empirical analysis involved two steps: Factor analysis to determine distinct attributes of data, and multiple regression analysis to test the relationships between data attributes, controls and performance measures.
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