As Enterprise Risk Management (ERM) continues to advance as a concept, linking financial, operational, and GRC risk management across the enterprise, new opportunities are emerging from the application of Big Data. Business is about risk; and management of an enterprise is about risk management. Risk and opportunity are inextricably strictly linked, per the famous and somewhat mistaken Chinese character which has been a business management meme since the 60s.
Application of Big Data and Advanced Analytics to risk management can create enormous potential for a change in how firms are run. While current application of analytics to this area tends to remain relatively small and limited to niche areas such as fraud detection, immediate market changes, regulation and bug forecasts, and the like, the capabilities are growing exponentially. Application of Advanced Analytics directly to business processes can create a mechanism of advanced performance in which individual processes are modified in accordance with an immediate analysis of risk. Such modification and would enable an intelligent form of agility, permitting companies to respond to events where they might occur within markets, supply chains, business conditions, and other areas. This will have different effects in different industries, initially impacting financial services and other professional services that can be immediately tailored to meet changing conditions. Yet we can see the potential for integration with manufacturing, software development and the like. Continue reading Risky Business: Incorporating Analytics in the Engine of Risk
As Big Data continues to develop as a force shaping the enterprise, we can expect to see changes in business processes. Saugatuck has been monitoring the intersection between Big Data and intelligent business processes for the past several years. Developments in Big Data and advanced analytics have already had a substantial impact upon concepts of Business Process Management (BPM) including linkages with Internet of Things (IOT) and the industrial Internet; and the application of advanced analytics to analytic processes themselves. This contributes to a Big Data/Process convergence that is likely to have a continuing effect within the enterprise environment.
The application of Big Data directly to business processes such as manufacturing, finance, and supply chain processes, combined with autonomous operations enabled by real time evaluation and prediction, creates a new fabric for business operations. While much of this area has been focused upon manufacturing and is now being studied by governments and businesses, the digital business surround means that processes created within one domain are easily transferred to others. So, as rapid advances occur in linking processes to the Internet of Things through the Industrial Internet, we can expect rapid application of these ideas to other business areas such as human resources, healthcare, professional services, and the like. Continue reading In the Valley of the Blind, Autonomy is King
What is Happening?
It was recently reported that privacy campaigners had walked out of talks aimed at creating a code of conduct for use of facial recognition technology. The talks, brokered by the US National Telecommunications and Information Administration (NTIA) included the Electronic Frontier Foundation (EFF), the American Civil Liberties Union (ACLU) and the Center for Democracy and Technology (CDT), among others. The groups were concerned over refusal to accept a need for prior permission from people being identified.
For those watching development of Big Data initiatives, this raises a number of warning flags. Privacy issues in data collection have raged for the past decade, but much of the talk has been theoretical and generalized. Facial recognition, however is very practical, concerns specific technologies, and is about practices that are not only possible, but are in use today. The issues raised by facial recognition are also relevant to a wide variety of other sensor-related items such as geolocation, audio sentiment analysis, M2M data logs, other image analysis, and even some forms of social media analytics. Continue reading Facial Recognition Raises Major Concerns for Big Data Privacy Regulation
What is Happening?
ISVs can, should, and do profit from the use of advanced data analytics – not only by integrating them within software and services offerings, but more importantly, by integrating an increasing range and scope of data (including Big Data) and analytics into their own business operations and decision making. Data regarding user behavior, operational efficiencies, and relationship management can and should be analyzed to help determine and take advantage of customer / buyer desires and needs, as well as competitive abilities, solution improvements, development strategy, upsell / cross-sell opportunities, pricing, business models, and hiring / retaining the most useful employees.
These were among the lessons reported by Saugatuck Research Fellow Bruce Guptill, who had the pleasure of attending and participating in this week’s “Deciphering the Data Storm” event, presented in Boston by the Software & Services division of the Software and Information Industry Association (SIIA).
Key lessons learned and reported by ISVs regarding the analysis and application of a wide range of business data (including Big Data) include the following:
- Data needs “gravity” in order to be useful; i.e., data needs varying combinations of human business context, situational relevance, and environmental semantics (i.e., “the voice of the author”) in order to be qualified, let alone be useful in analysis.
- Don’t always focus on reducing / limiting the “bigness” of data. Adding to / augmenting data with similar, complementary, and relevant data can provide and improve the “gravity” of that data. The key information sought may not be found completely in your own data. That being said, don’t be afraid to apply a variety of filters to screen Big Data; just be willing to accept failure and move on quickly when the filtering doesn’t work as expected.
- Share data in common to improve collaboration. “Success” is defined differently everywhere, even within small ISVs. Utilizing common sets of data has more beneficial impact, and enables more and better business collaboration, than trying to develop and focus on a “single version of the truth.” Different groups will always have different perspectives, and use data in different ways; ensuring that the data used is common rather than simply absolute will enable better understanding, and foster more (and more useful) interaction.
- Know what the next step is. In other words, set realistic business goals beyond simply analyzing data. Once deeply into the analysis, it’s easy to lose sight of business reasons behind the analysis. And as more data becomes more readily available from more sources, it becomes more and more easy to become overwhelmed.
Continue reading SIIA in Boston – Deciphering Data and Analytics for ISV Business
Within just a few years, Big Data has become a driving force in enterprise IT, with projects springing up in every imaginable area of endeavor. It will drive processes as well as aiding in innumerable areas of information processing and discovery. But integrating Big Data with current infrastructure is problematic. To date, projects remain relatively discrete and can be based upon limited integration with data warehouses or separate operational data stores. But a more centralized and better-integrated solution is required as a basis for capitalizing on wide-ranging new opportunities. An important concept which is currently being developed is the “Data Lake,” which provides a repository for gigantic volumes of unstructured data in native format and may also include structured data to feed data warehouses. Continue reading Data Lake and the Data Lakers
Strap yourself into your seat for the big data security analytics show, for it’s coming to a town near you. Carnival barkers from every walk of life will want you to come into their tents to see the latest and greatest show on earth: the big data security analytics show.
You will want to understand why using evolution charts, Venn diagrams, Pareto charts, and Pivot tables can or will help. You’ll want to see what association rules, clustering, decision trees, and forecasting can do for you. And you will want to understand the difference between analysis and knowledge, as it’s applied to security.
You will also want to make the distinction between whether you have to hire a data scientist or not and whether this will solve your immediate problems. You will also want to consider which approaches you could take that will produce the most value in the short, medium, and long term for your company and career.
To be useful, security analytics must take the large volume of data that can be collected and take three actions with the data, as follows:
- Reduce voluminous data and identify the pattern that matters,
- Use the information to enable a timely and appropriate in-situ response, and,
- Use the data to make adjustments – after the fact.
Continue reading Security’s Next Era: Big Data Security Analytics
Perhaps geographic information systems (GIS), usually a specialized backroom capability, are emerging from the dark shadows of enterprise basements. The past year, saw notable changes and advancements in geospatial data and services relevant to Digital Business. These changes included integration of GIS with enterprise financial, sales, marketing, and collaboration systems and integrating enterprise development environments with location intelligence solutions to support Cloud location services.
Large-scale emergencies or disasters require sudden and dynamic resource allocation to meet the demands of geospatial professionals, related domain sciences, and the large amount of compute-capacity necessary to perform analytics on what can be terabytes of spatial data. Server-based solutions cannot typically fulfill the new access requirements. Continue reading New Uses Drive Geospatial Integration with Cloud-Based Enterprise Solutions
1. How would you define a Digital Business?
- A Digital Business or Enterprise is built upon digital technologies to create value for customers via innovative business strategies and interactive experiences that leverage an easy-to-use-and-access platform on demand.
- Internally, Digital Business empowers knowledge workers through data and collaboration, 1) enabling analytics-based insights and behaviors and 2) the ongoing enhancement of digital offerings.
- The foundation of Digital Business is the Boundary-free Enterprise, which is made possible by an array of time- and location-independent computing capabilities – Cloud, Mobile, Social and Data Analytics plus Sensors and APIs — with Integration as the glue to enable synergy and leverage business value.
- Digital Business should not be thought of in isolation, but rather as an ecosystem of the enterprise from 1) suppliers to buyers, embracing 2) business partners and technology partners and empowering 3) employees to serve their 4) customers and address their markets more effectively.
2. Why does having a digital business strategy matter for staying competitive?
- There is always a faster gun, a sweeter smile, a more convenient offer. Continuous enhancement of digital offerings is essential.
3. What are the main drivers requiring businesses to adapt by developing digital strategies?
- Better, faster, cheaper is just table stakes. We are talking about more innovative, more effective solutions that engage and retain customers. For two disruptive examples from the travel industry, consider Uber and airbnb. Uber, for example is a location-aware and pre-paid. The nearest car finds you, and the fare and tip are already paid automatically. An airport trip I took in Tampa was cheaper than the bus. airbnb is personal and intimate, but discreet and a bit of an adventure. Not too much, of course, because there are reviews, but it’s definitely not a manufactured hotel/motel experience.
Continue reading Six Frequently Asked Questions (6FAQ) About Digital Business
Traditionally, mobility was a means of personal interaction and accessing business systems, data, and operations. Mobile technology means more than just personal enablement. Now, mobility is also a means of gathering/producing business, which in turn generates and requires an increasingly wide and deep volume and variety of data. For example, businesses can improve their customer engagement by using mobile devices to collect more feedback around the customer experience. Some mobile applications use location to expand and improve marketing efforts to customers. There is a global surge in mobile payments both via card scanners and mobile money.
It’s not just mobile phones and tablets; wearables and sensors on movable items such as vehicles and retail goods contribute to the mix of devices generating and consuming data and bandwidth. Accompanying the resulting deluge of data is uncertainty. Uncertainty abounds concerning data volumes, network capacity, security, privacy, and other processing requirements. Cloud implementations can help address this uncertainty by handling the fluctuating data and communications demands while ensuring availability and reliability. Continue reading How Mobility and Big Data Empower the Boundary-free Enterprise™
What is Happening?
Recent software analyst and IT media reports, including insights from a recent SAP Americas User Group (ASUG) survey, suggest that SAP’s HANA Big Data service / platform is not yet seen by a majority of ASUG members as benefiting their business (relative to the implementation cost of implementing), or driving enough revenue growth for SAP. SAP has, very smartly, issued a careful rebuttal explaining how, where, and why customers see value in HANA – and more importantly, offering to work with any customer to help them understand and realize business benefits from the offering and its associated apps.
We believe that, through at least 2016, this type of approach is the most effective way of getting user enterprises to understand the value of any Big Data analytics capability; i.e., develop company-specific and operationally-specific business cases in order to enable and develop business value. This is because, in most companies, Big Data analytics just can’t be widely used to deliver broad-based business benefits across the full portfolio – because user enterprises have huge challenges finding and managing their own data, let alone analyzing it. Continue reading The Business Problem with Big Data Analytics