Viewing data as an asset implies there are benefits to taking a supply chain approach to data management. It’s not just inventory that needs to be at the right place at the right time in the right format and quantity. An end-to-end information supply chain approach, from sourcing/acquisition through transformation and storage, to end users, analytics and insights, allows you to keep your focus on the business problems to be solved, and avoid having that ‘big honking data cube’ become a bottleneck instead of an enabler.
Here are a few best practice supply chain mindsets to consider applying to data management:
Inventory turns / Data turns: How fast can you acquire and get the needed data into the hands of the decision makers? The whole point of analytics, BI and corporate performance management is to make better decisions faster, and how you structure your information supply chain will have a big impact on that “faster” part. This could mean anything from self-service BI to data visualization to better and faster data prep and data quality procedures. Critical decision support data delivered too long after the problem surfaces is like not having the inventory you need in the stores until the holiday shopping season is half over.
Analytics / Decisions at the edge: Taking “faster” to the extreme can sometimes mean taking action on the data BEFORE you store it, acting on streaming data via event stream processing, which has applications in financial services (e.g. credit scoring), cybersecurity and fraud (e.g. detecting unusual network connection patterns), quality (e.g. process and product quality control), and asset maintenance (e.g. sensor data from critical equipment). Whether it’s humans or machines making those decisions at the edge, real-time and near-real-time decision support capabilities are becoming competitive differentiators across a number of industries.
Visibility / Control Towers: Reacting quickly to fluctuating demand requires visibility into your entire physical supply chain, from what’s in which store or warehouse, which production lines are down for maintenance, and which suppliers have additional capacity at the ready. In the same way, better business decisions means having immediate access to ALL the relevant data – a worthy integration challenge for both inventory and data, with a commensurately worthy business outcome.
ABC inventory / data classification: Not all inventory is equally important, a maxim understood by every supply chain professional. “A” inventory is high in value but not necessarily volume, warranting tighter controls and monitoring than low-value, high volume “C” material. Your data can likely be similarly categorized, with revenue, cost, employee and customer data no doubt in the “A” category, with perhaps production, quality and transaction details falling lower in priority. Such a classification approach, based on what data is used most often in the most critical decision support processes can help your prioritize your data quality and myriad other IT activities and investments.
Data Management for Analytics: Designing a physical warehouse that maximizes the efficiency of receiving and storage is not necessarily the best overall approach to supply chain management, where access to raw materials and WIP at the right place and time on the factory floor is more critical to meeting cost, revenue, customer satisfaction and business goals than simply minimizing storage and handling costs. Likewise, a data warehouse built to minimize data storage cost may make it difficult for business and decision support users to access what they need, in the format they need for rapid analysis and insight. This gets back to point number one above about data turns: it’s not about how fast and cheaply you can get data into the warehouse, it’s about how quickly you can turn that data into valuable insights across the entire information supply chain.
Why a supply chain mindset is critical to maximizing the value of your data assets harkens back, once again, to Brian Arthur’s key insights in “The Second Economy”. The first, industrial, economy was an economy of bones and muscle and sinew but without a nervous system. Or perhaps more correctly, humans comprised the entirety of the nervous system of that first economy. We are currently in the throes of a second economy which, via computers, data, IT and the internet, is giving the first economy a brain and nervous system independent of direct human involvement.
But there is a third economy coming. The second economy is simply adding a nervous system to the existing industrial structure as it stands. The industrial structure is still primary – the data is generated by, and follows, the product / service. But with the advent of AI, sensors, machine learning, robotics, 3D printing, flexible factories, the IoT, and the digitization of not just manufacturing processes but entire economic segments, such as banking and communications, the data becomes primary. The product or service follows the data.
In the flexible factory of the future, all production lines will be able to make each and every product. The data will dictate that line A be configured for brand X, with line B configured for model Z, and the inventory / material / product will follow. In healthcare, the patient will follow the data through each stage of prescribed care based on initial diagnosis, from admission to specialist to labs to surgery to post-op to rehabilitation. Omni-channel retail means that the product will follow the data from factory floor to doorstep drone delivery. Autonomous vehicles will go where the data tells them to go.
Getting to this third economy, however, will require adopting a supply chain approach to data, where end-to-end information flows are optimized for speed, insight, value and business / customer outcomes.
By Leo Sadovy, EPM Channel Contributor, from: http://blogs.sas.com/content/valuealley/2015/10/27/your-information-supply-chain/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+ValueAlley+%28Value+Alley%29
Leo Sadovy handles marketing for Performance Management at SAS, which includes the areas of budgeting, planning and forecasting, activity-based management, strategy management, and workforce analytics, and advocates for SAS’ best-in-class analytics capability into the office of finance across all industry sectors. Before joining SAS, he spent seven years as Vice-President of Finance for Business Operations for a North American division of Fujitsu, managing a team focused on commercial operations, customer and alliance partnerships, strategic planning, process management, and continuous improvement. During his 13-year tenure at Fujitsu, Leo developed and implemented the ROI model and processes used in all internal investment decisions—and also held senior management positions in finance and marketing.Prior to Fujitsu, Sadovy was with Digital Equipment Corporation for eight years in sales and financial management. He started his management career in laser optics fabrication for Spectra-Physics and later moved into a finance position at the General Dynamics F-16 fighter plant in Fort Worth, Texas.He has an MBA in Finance and a Bachelor’s degree in Marketing. He and his wife Ellen live in North Carolina with their three college-age children, and among his unique life experiences he can count a run for U.S. Congress and two singing performances at Carnegie Hall. See Leo’s articles on EPM Channel here.