Big Data Management the Focus of Workshop Before RoboBusiness 2018 | Robotics
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In automation, the whole is often greater than the sum of its parts. From industrial robot arms to automatic ground vehicles and aerial drones, businesses are beginning to recognize the value of the big data that embedded sensors generate. But machine learning needs specific data, and deriving and applying insights from big data requires a real strategy. Where should an enterprise start? Fortunately, experts will soon be gathering to discuss the challenges and promise of big data management.
In the afternoon of Tuesday, Sept. 25, a pre-conference event before RoboBusiness 2018 in Santa Clara, Calif., will focus on “Planning and Implementing a Successful Big Data, Data Management, and Analytics Strategy.” Attendees will have a chance to learn from Bill Roberts, director of the global IoT practice at SAS; Allen Thompson, director of knowledge management and analytics at Oneida Nation Enterprises; and me as we discuss how you can get the most out of big data. Here’s a preview.
As part of building a coordinated big data strategy, we’ll examine the need for basic (and not-so-basic) tasks, including big data management, data cleansing, and deduping, as well as how artificial intelligence and predictive analytics are becoming critical to automated environments.
This year, RoboBusiness includes four conferences to make it easier for you to find the information you need most. Whether you are involved in running a robotics business, designing products, or implementing robotics solutions in your company – we have a conference to meet your needs.
Why big data management is important in manufacturing
While master data management, condition monitoring, and IT asset management may not be top of mind for many business leaders, they are the foundation for the success of smart factories. Robotics has spread from the major automakers to smaller manufacturers, and big data management tools could do the same.
The health of manufacturing operations and the availability of materials and labor are critical, especially to organizations monitoring network and device availability in light of increasingly stringent service-level agreements (SLAs). Such efforts require real-time management of resources to meet or exceed SLA metrics around system and device uptime and equipment availability.
SAS’s Bill Roberts has experience with manufacturing, predictive maintenance, manufacturing, IoT, and supply chain.
Also, as manufacturers look to fine-tune production in response to demand, many are seeking innovative ways to meet the needs of their partners and customers. Increasingly, this leads to digital transformation initiatives, as well as the use of big data management, customer and predictive analytics, and AI and machine learning.
Understanding and anticipating product demand and customer expectations is more difficult than ever, with organizations having to store massive amounts of big data. This can be as much as hundreds of terabytes of both structured and unstructured data. This big data often resides in databases and data warehouses throughout these companies, sometimes in siloed, disparate systems.
Analyzing all this data requires comprehensive reporting, business intelligence (BI), analytics, and AI systems. Does your organization have a plan for these tools and practices? McKinsey estimates that the volume of all data continues to double every three years, as information from digital platforms, wireless sensors, and mobile devices like robots are shared across systems through the emerging Industrial Internet of Things (IIoT).
Lean operations require accuracy and reliability, and organizations can monitor production and avoid costly downtime through predictive analytics. Organizations also want to successfully engage and interact with both channel partners and sales prospects. Deep learning and big data management tools can help meet these goals. These solutions can offer timely and relevant alerts and next-best-action suggestions, which can augment customer outreach efforts and improve the overall customer experience across industries.
Big data has great potential for retailers, offices
The retail and manufacturing sectors are well-positioned to reap the benefits of big data management and analytics. For example, McKinsey found that over the past five years, U.S. retail supply chain operations that have adopted data and analytics solutions have seen up to a 19% increase in operating margins.
Allen Thompson has worked on data, analytic, and intelligent solutions for casinos, banks, and retail operations.
While businesses have realized much value from big data management and analytics to date, there are ample opportunities for improvement. The utilization of data and analytics has been uneven, with the retail industry capturing only approximately 30% to 40% of potential value from such systems. Manufacturing has captured only about 20% to 30% of potential value, reports McKinsey.
Also, PwC estimates that almost half of all workforce tasks could be automated through robotic process automation (RPA), which could translate into a $2 trillion reduction in global workforce costs. RPA is already used to resolve credit-card disputes, process insurance claims, and reconcile financial statements, to name just a few tasks where AI and machine learning are being used.
Big data tools increasingly available
Beyond supply chain and manufacturing, every industry, from agriculture and healthcare to retail, is increasingly reliant on big data management and analytics. As product developers, engineers, and strive to better understand internal processes and customer demand, they must navigate through unprecedented amounts of data before they can draw any meaningful conclusions. Sifting through structured and unstructured data residing in various databases and data warehouses can be a daunting task without the use of modern big data management tools.
In addition, there is the ongoing challenge of making sense of disparate data so it can be interpreted as actionable information. Consequently, it’s not difficult to see why many organizations are reluctant to initiate analytics initiatives at the business-unit level.
Come to the workshop before RoboBusiness to learn more about big data management strategies.
Not only is it difficult to mine such data, but until recently, most software for the task required costly analytics solutions. Even with sufficient investments in hardware and software, organizations also needed the expertise of data scientists and statisticians assigned to their quant staffs.
However, a revolution of sorts is occurring, with progressive BI and analytics providers, such as Microsoft, MicroStrategy, Tableau, SAP, SAS, and Qlik introducing intuitive and powerful products. In addition to being relatively easy to use, these solutions also offer connectivity to a wide range of data source. In many cases, such big data management and analytics products also come with a lower total cost of ownership.
An increasing number of vendors are offering graphical user interfaces (GUIs) and data visualization as a way for users to construct models via visual representations of the data. These methods can enable business users to create queries and models without the need to write and sequence SQL queries.
With these solutions, the rules and sequences for data evaluation are set by manipulating visual elements — much like setting joins and formulas in some report-writing programs. The underlying text-based code is available for experts who want to review them in greater detail. The result has been a new class of data-visualization and analytics products on the market.
Ease of use democratizes big data management
While GUIs allow quant staffers and business users alike to use big data management and analytics, the latter now have tools from which they can run various predictive models and scenarios. This capability was not generally available until recently. These easier-to-use solutions do not replace highly trained and experienced quant personnel, but they often allow better use of their time and expertise.
In an increasing number of organizations, the quant staff and operations, sales, and marketing staffers can now work much more closely as teams. In such cases, the data experts often guide the business users in a first pass at modeling test-and-learn scenarios. Data experts are collaborating in the quality testing and fine-tuning of the models, making them more efficient and scalable.
This collaboration also helps business users better understand underlying demand and model “what-if” scenarios. This can include some clustering and propensity models, as well as testing pricing models and making predictions about the probable demand under various scenarios.
These are exciting times for business users interested in using big data management to better understand their business drivers. The many options available with the latest analytics and data visualization solutions offer organizations a deeper understanding of their customers’ purchasing habits, anticipated needs, and likely future behaviors.
All of this information can help to deepen relationships and improve the overall customer experience. Looking ahead, there are tremendous opportunities to increase the value derived from big data management and analytics. The benefits of such systems can be realized across industries.
Register now to attend this pre-conference workshop and RoboBusiness 2018.