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.
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.
As part of Saugatuck’s ongoing “Finance in the Cloud” Series, Mike West and Bill McNee recently conferenced with executives from Tidemark to learn more about the Tidemark solutions, their market reception, future plans for enhancement and how Tidemark views “Finance in the Cloud.”
Formerly known as Proferi, Tidemark is a privately-held firm founded in 2010 that provides Cloud-based analytics solutions built for a mobile device-enabled platform. Tidemark has grown rapidly, and now has nearly 200 employees and over 50 customers.
Tidemark’s differentiation in the financial planning and analysis (FP&A) space, which has many competing providers, is a process-focused elastic grid platform that enables a high ease-of-use user experience designed to move financial analysis beyond the CFO suite. Tidemark targets the emerging urgency – not just in the CFO suite, but across the lines of business – for real-time analysis of multi-dimensional, complex Continue reading
With Clouds come storms, and big storms tend to blow things around. Back in 2012, we began building our “Boundary-free Enterprise™” business concept to illustrate the concept of how much Cloud and its related technologies will “blow away” many, if not most, of our traditional business and technological boundaries.
A new Strategic Perspective for Saugatuck Technology’s subscription research clients looks at the four types of boundaries most likely to be buffeted by these storms, as follows: Continue reading
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
Recently, Saugatuck attended the 2014 Alteryx Inspire event in San Diego as part of our ongoing Analytics, BI, and Big Data research. The event showcased not just Alteryx offerings and customers, but also did a good job of presenting and encouraging discussion around Analytics trends, partner relationships, and challenges for users of analytics – including Big Data. We came away from the event with three key insights, as follows:
1) ETL & Access to the Data. One of the primary differentiators of Alteryx is built-in ETL. Even though the application features significant Advanced Analytics capabilities (built around the R language), the Alteryx Designer focuses around an ETL-centric workflow. These capabilities make Alteryx adept at combining multiple data sources, and performing complex Joins and Transformations that would normally be prohibitively difficult for end-user business analysts. These capabilities feature centrally for the customers that we talked to as well, as most Continue reading
What is Happening?
Digital delivery models are impacting traditional businesses. Driven by consumer demand for convenience and new consumption models, including subscriptions and usage-based consumption, enterprises are moving to Digital Business models and offerings, and finding huge upside from predictable revenue streams and long-term, recurring-revenue customer relationships.
There’s no simple means of accomplishing this, however – there’s no “silver bullet.” Saugatuck’s research in the constantly-changing, constantly-innovating world of Digital Business continues to indicate that success requires not only significant investment in business strategy, modeling, and organization, but also a flexible, platform-based approach built on three cornerstones: data analytics, dPaaS and other Cloud development platforms, and DevOps.
A 36-page new Saugatuck Strategic Research Report – Cornerstones for Digital Business: Big Data, dPaaS, and DevOps – summarizes and explains this platform-based approach, using key concepts of Digital Business, in-depth explanation of the required platform architecture, and re-examinations of foundational Saugatuck research to illustrate and guide readers through Digital Business transitions. Continue reading
On September 25, 2013, Saugatuck held its 3rd annual Cloud Business Summit at the Westin Times Square in New York City. As with prior Summits, our event brought together more than 100 large-enterprise CIOs, CTOs and senior business and finance leaders – to explore how they can and are realizing value from the Cloud. This year’s conference theme was “Rethinking Business Innovation.”
One of the hottest topics in Cloud business today is Big Data and advanced analytics. While many current attempts and instances are still trials and PoCs, as enterprises work to figure out what these “power tools” can really do – significant progress and payback is being demonstrated in analyzing purchase decisions and in the targeting of customers, and in leveraging sensor data to help drive operating efficiencies throughout industry and the supply chain.
In this featured panel, Saugatuck Technology SVP and Head of Research Bruce Guptill is joined by Will Klancko, Sr. Risk Program Manager at GE Capital, Hodan Hassan, Managing Director at Unicef Continue reading
Industry research, including work done for clients by Saugatuck Technology, has consistently indicated a lag in adoption of Big Data and Advanced Analytics within the financial area – including Finance processes in business, and in the Financial Services industry itself. This has been attributed to natural caution regarding this data and these activities, so central to business. But the reasons for this lag go deeper, and are more complex than might be supposed. In consequence, as the Big Data revolution begins to fully engage finance, there are likely to be significant repercussions, and great opportunities for vendors.
The Financial Services industry and the enterprise Finance function are inherently linked. In some large companies, there is little difference between department operations and those of an investment firm or a small bank. New technologies and concepts are tested in both environments, creating a healthy cross-pollination of innovation. Shared issues include the need to optimize investments, special regulatory concerns, special security concerns; and a host of measures for understanding, measuring, and predicting financial results. Continue reading
A new Strategic Perspective published for Saugatuck Technology clients looks at how and why simply reporting on what has already happened, or monitoring various aspects of a business, is unlikely to provide adequate insights needed to make high-impact decisions and to better the competition. Enterprises are therefore combining internal operations and external data streams in new ways, which is blurring the traditional lines between these two entities, while providing new actionable information. The result: Fast-growing interest in Predictive Analytics, especially as regards using Big Data. Many businesses have and are moving to predictive and prescriptive analytics as a way to get better returns on their Big Data investments by determining causation from predictive analytics. Continue reading