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Exactly what is the relationship between workforce analytics and big data? Most of the use cases for big data seem to revolve around  marketing and customer service, IT, or risk management, where large volumes of unstructured data sources reveal big value. Our research into big data analytics suggests a prime benefit of big data is in the ability to retain and analyze larger amounts of data and to increase the speed of analysis. However, workforce analytics does not suffer from an overwhelming amount of data, but rather from the inability to apply meaningful analytics to the data organizations already have.

Ventana Research has been covering workforce analytics and the broader world of human capital management for years. While workforce analytics may seem like new wine in old bottles for some, it is gaining visibility, and companies are taking note. Tom Davenport’s articles in Harvard Business Review outlining the linkages between employee and company success, while well-publicized, is not particularly new. The book “In Search of Excellence” came to some of the same conclusions 30 years ago. However, fundamental shifts in the workforce and macroeconomic and demographic changes are driving these arguments forward alongside new demand for workforce measurement and analytics:

  1. Structural economic shifts: Company success 30 years ago was grounded in efficient use of tangible assets such as plants and equipment. Today, company success is more grounded in intangible assets such as intellectual capital and brand loyalty. These assets, in turn, are driven by employee engagement and performance, or human capital, yet companies lack the capability to effectively measure and analyze their investments in this area.
  2. Demographics: According to data from the 2010 U.S. Census, America’s 78 million baby boomers have begun turning 65 at a rate of one every 10 seconds, or 3 million to 4 million per year. Knowledge transfer and succession planning are beginning to move to center stage and at the same time a larger volume of the millennial generation are in the workforce and newer generation Facebook generation are coming into the workforce quickly.
  3. Compliance and globalization: With significant increases in regulation, such as HIPPA in health care and Dodd-Frank in finance, companies need to be able to manage employee data in ways they never needed to before. Furthermore, trends in globalization and outsourcing introduce new analysis scenarios that impact the very essence of companies.

Within the area of workforce analytics, none of these trends necessarily intersects with big data such as unstructured sentiment, speech or large volumes of machine data. Such big data sources do not currently help us with performance reviews or compensation management, two big areas of focus according to our benchmark research on workforce analytics. The larger challenge for workforce analytics is getting a handle on the data that is sourced across cloud computing and enterprise systems that can be integrated and generating measures and metrics more automatically than they are today. Then we need to get smarter analytics that are more than just key performance indicators with charts in a dashboard. Maybe a dose of predictive analytics to indicate likelihood on an employee leaving the company would be good.

Big data drivers such as social come into play when we look at overall recruitment, for which we recently did a benchmark research study, but it’s a different question when we ask whether the data can be analyzed. For example, recruiting is a big part of talent management, and we are beginning to see more use of social media in this area. However, when we think about the analytics side of social recruitment, we might look at metrics such as time to fill a position, or cost per hire, but our analysis is not only focused on the content of the recruiting conversation, for example. The metrics we use are still straightforward and use data from what may be called our systems of record.

Over time, I expect that big data will move into the world of workforce analytics, as companies start to manage their brands and start to do linkage analysis tying social media buzz with their ability to acquire and retain the right employees. For now, companies have their hands full trying to apply these types of analytics just to their customers, where outcome metrics such as revenue per customer or frequency of visit are much easier to deal with than concepts like employee engagement or employee productivity.

In sum, companies considering workforce analytics shouldn’t worry much about big data because their HR departments have bigger challenges in realizing the value of analytics on in-house structured data and do that efficiently. As our benchmark research shows, these challenges revolve more around people and process. HR has not exactly been a hotbed of analytics to date and it needs to build such skills or transfer them in from other parts of the organization, such as finance.  Eventually it will move on to trying to answer questions of linkage analysis and finding drivers of revenue and profitability within this discipline. Isolation techniques and quasi-experimental design skill sets will be in demand. Companies are starting to wake up to the importance of workforce analytics, and new tools are starting to drive traditional BI capabilities into this analytics world. For these reasons I am excited to begin the design our next workforce analytics benchmark. If you have any feedback on your challenge in workforce analytics, please let me know!

Regards,

Tony Cosentino

VP and Research Director

With more than 90,000 attendees registered and 100,000 more expected to watch via live stream on Facebook, Salesforce.com’s Dreamforce is the biggest technology event of this year. The conference kicked off yesterday morning with MC Hammer letting the packed house know that it was “Chatter time” and leaving little doubt about the theme of the Marc Benioff’s keynote speech: Social. Citing numbers from McKinsey and IBM, Benioff suggested that social adds $1.3 trillion to the economy and that CEOs see social media as the second most important communication channel of the 21st century, just after the direct sales force. Our own sales benchmark research here at Ventana Research shows similar trends, with 63 percent finding that collaboration is a key trend in sales organizations.

The keynote pronouncements were put in context by a number of clients. Two clients in particular highlighted broad changes occurring in industry.

Rossignol uses Force.com to exploit social and mobile areas for competitive advantage. Rossignol is a winter sports gear retailer, and its target consumers – as well as its dealers, such as REI – are often youthful and cutting-edge.

Saleforce.com allows Rossignol’s sales representatives to adjust offers and sign deals at the point of purchase with mobile devices. It integrates customer social profile information, offers team interaction between the pricing departments and other managers, and lets salespeople revise proposals and sign deals on the spot. With impressive ground game capabilities like these, Rossignol will likely take market share until its competitors can deploy a similar approach.

Salesforce also allows the ski community to be involved with Rossignol’s brand at a visceral level through things like coaching camps and excursions, and provides informal interactions with like-minded skiers. Even more impressive is the involvement of the company’s communities in its two-year product development cycles. This sort of crowdsourcing was unheard-of just a few years ago.

Rossignol represents the impact this type of company and brand is having on the relationships between manufacturers and retailers. Retailers are losing leverage with customers as manufacturers build loyal followings and establish pull-through channel strategies among their end-user customer base. We see similar trends in other markets.

Salesforce also highlighted General Electric, my alma mater. When I was at GE, collaboration consisted of doing things like GE Boundaryless Sales, where reps would share leads across GE Capital as well as with the core divisions. So GE has always been somewhat collaborative, but I was surprised to see how aggressively it is pursuing things such as social collaboration with its sales teams.  In particular, it is rolling out Salesforce.com in the Honeywell division, which in and of itself is a large diversified business. Deal sizes go from a few thousand dollars that may take a couple of weeks, to a seven-year, $100 million deal.

Two interesting things here apply to the broader GE organization, and illustrate how sales is changing as a result of social and mobile. First, the new systems give management a better picture of what is happening and how to manage its sales force. It’s difficult to do forecasting, communication and root-cause analysis with very different buying environments. Internally, these types of diversified organizations are often a Tower of Babel. Social tools provide the equivalent of a common language that can be instituted across the organization.

The second big impact is the inversion of the organizational pyramid. This is not just a GE phenomenon, but is beginning to occur across multiple industries.  The business process used to be about getting information from the sales force into the organization –  for instance, getting pipeline updates to management and rolling them up, or trying to get a salesperson to share his contact information. Now it’s just the opposite; organizations are pushing information down into the hands of the sales folks and empowering them to make deals. This is changing the nature of sales. It used to be that a lone wolf was the ideal salesperson. Now it’s a social collaborator who can act like an orchestra conductor.

As these two companies demonstrate, seismic changes are occurring in organizations and across industries. I strongly encourage companies with medium-sized or large sales forces that haven’t yet moved forward with sales force automation (SFA), or consumer brands that are not actively engaged in community building, to start doing so.

For additional in-depth analysis of the social aspect at Dreamforce as well as the key announcements, read my colleague Mark Smith’s post. Salesforce is helping companies change the way they operate and through the use of social and mobile technology in conjunction with cloud computing which is definitely worth looking at more closely.

Regards,

Tony Cosentino

Vice President and Research Director

Thinking about big data and the swirling world of analytics that surrounds it can be overwhelming. Broad-based organizational and technological changes are driving a new industrial constitution built on time-to-value and closed-loop systems of organizational and machine learning.  As I analyze our next-generation business intelligence benchmark research results, I see trends in collaboration and mobile technology that will have a profound impact on business for generations to come. Given these defining times and technologies, how does one go about thinking of big data and the business analytics value chain?

Until now, we as an industry have been looking at the three Vs of big data: volume, velocity and variety, which have provided a way for people in the technology industry to think about the emerging ecosystems of big data. Most business people, however, have not heard of the three Vs of big data. For them, we need to move beyond the technology-oriented three Vs and provide a simpler way to think about the impact big data has on business.

The approach I suggest is to look at what might be called three Ws of data. (It doesn’t matter whether we’re talking about big data or small data.) The three Ws are the What, which refers to the data and information itself; the So What, which refers to the analysis of the data or the process of deriving implications and meaning from the data; and the Now What, which refers to the decisions made from the data and the resulting actions. (The actions after the decision may also be referred to as the Then What, but for simplicity’s sake we’ll include it in the idea of Now What.) The more we can think about technology and information in a holistic business process and people-oriented manner, the better we can deliver time-to-value (TTV) associated with big data. Let’s dive into each of these Ws a little more and discuss how they relate to technology and the three Vs.

Most technology analysis focuses on the What of big data, for good reason. It’s an exciting space and these are exciting times. The transformation of information system architectures stokes the imagination and opens conversations about timely processing for large sets of unstructured and semi-structured data. Approaches include massively parallel processing and in-memory processing, and may involve entirely new approaches such as Hadoop. In this area, serious information management and quality control issues still need to be addressed in order to make sure our data is trustworthy and actionable. Our benchmark research on business analytics shows that analysts spend two-thirds of their time just preparing data prior to doing actual analysis.

At the same time, we need to move beyond the What. The So What puts us squarely in the arena of business analytics, and in fact constitutes a large part of my own research agenda for 2013. Business analytics involves more than just applying mathematical and statistical approaches to information; it’s about creating useful and actionable insights that support both strategic and tactical decision-making. No matter how much analysis you do, if data just sits in a file or in a dashboard report and nobody takes action because of it, there is no value in the data. Business analytics is a broad area about which we at Ventana Research have built an expansive  body of research that extends both into lines of business (LOB) and vertical industries. We’ve been able to establish key term definitions and performance baselines, and tease out the reality from the hype. I will extend this body of research both to highlight trends and to show how newer forms of analytics such as machine learning systems and disaggregated modeling impact organizations’ approaches and decisions. Working backward from the business problem to be solved, I will investigate the tradeoffs businesses need to make when they look at real-time streaming analytics, near-real-time analytics and fully batch-processed analytics. I also plan to explore how analytics is broadening its usefulness from strategy into operations.

The Now What is about decision-making and action. Once analysts have done the exploratory and confirmatory analysis and are clear what the data says, people still need to make business decisions. At the end of the day, this is still a markedly human function. This is where meetings and discussion drive collaboration and mobility tools, not the other way around. It is where attitudinal data comes together with behavioral and profile data, and where institutional knowledge shows its strengths and sometimes its limitations. It is where applications and closed-loop processes need to be pushed out to the front lines of organizations in order to improve overall customer experience and increase brand loyalty. How these tools are to be rolled out and integrated is another area of focus for our firm. Right now there seems to be no single mind in the market as to whether BI platforms, applications or productivity suites will dominate that last mile to the end user. With Windows 8 coming out, cloud deployments becoming more mainstream, and tablets in the hands of just about everybody, 2013 is shaping up to be a defining year at every end-user and customer touch point.

The ground-breaking research that we are planning for 2013 represents just the tip of the iceberg in a very exciting age. Of course, we’re just in the planning stage, so let me know what you think about the road ahead. I look forward to hearing your feedback!

Regards,

Tony Cosentino

VP and Research Director

As I listened to the keynote address at, conf2012, the annual Splunk user conference, my initial impression was that the company was spreading itself too thin. The company highlighted four rather formidable areas of organizational focus: Enterprise 5.0, the company’s flagship data platform, which is now in beta; Development, which is support for building applications and integrating Splunk within the broader IT infrastructure; Content, the continued development of core applications and use cases in areas such as systems management and security; and Cloud, based on the recent Splunk Storm product, which targets a new class of customer – namely those developers who use services for everything. Is this broad-based vision a realistic goal, or merely an attempt to appease Wall Street pressure given the company’s relatively recent IPO?

The key to answering this question lies in Splunk’s second objective, Development. Splunk sees its software as a platform upon which the developer community can build applications and create value. This business model has worked for such venerable companies as Microsoft, Apple, Amazon and Facebook.  The key is gaining enough traction, and the main driver of such traction is Splunk’s extensible data fabric, with its 170+ REST-based interfaces and SDKs for Python, Java, JavaScript in beta, and PHP as a public preview. Such an approach allows Splunk to develop many use cases outside its core areas of systems and security, and cover the last mile to the business user with personal productivity tools such as Microsoft Excel and visual discovery tools such as Tableau.

In talking with Splunk customers, I found a real passion for the product. Every customer I spoke with was either in the process of expanding their implementation or planning to do so. When asked about Splunk’s competition, customers couldn’t provide any quick answers; most said that there really was not any competition for Splunk.

Some customers were going as far as having an autonomous Splunk team within their organization with an internal chargeback structure. This got me thinking about the fluidity of the software buying process within today’s organizations, as customer analytics and technology spending shifts toward LOB budgets. In this new age, Splunk is potentially a secret weapon of IT, since it gives the IT department the ability to go to other departments and show its business value.

The trick for Splunk will be to quickly expand the use and usability of its product. Systems management and security have catapulted the company over the proverbial chasm of technology adoption, but in order to live up to expectations, it will have to address the needs of the business users – and Splunk should be able to do that, based on the presentations I saw and executive discussions I had at the conference. A sampling of innovative use cases includes:

  • Expedia using Splunk to improve customer experience through increased response times, as well as optimize its search engine bidding process;
  • Intuit using Splunk to increase customer intimacy by looking at actual site behavior rather than having to get the stated experience from an already frustrated customer;
  • Comcast linking customer search behavior, location and billing information to customize content and usability;
  • Bosch using Splunk to tie together sensor data from a network of medical devices to increase usability, satisfaction and uptime for in-home care patients.

These are just a few public cases; I also spoke with large telecommunication companies and government organizations that are using Splunk in innovative ways to use data from systems at any velocity.

At the end of the day, the killer application for Splunk is the same as it was for Google: search. It solves the same problem of expanding sets of distributed unorganized data, and it solves it in much the same way; but note that Splunk uses its own implementation of MapReduce. Our benchmark research into information management shows that data spread across too many applications and systems is the number-one barrier to managing data, and as Google did, Splunk helps to solve this problem with a relatively simple and elegant solution. The only real difference is that Splunk targets the business market instead of the consumer market, and the engine returns machine language instead of human language.

Of course, the Google-like search function is a double-edged sword. The ease of use of search and iterative discovery analytics that Splunk provides its users can add tremendous value. At the same time, the interface is not a business user interface. It will need to expand further with data and visual discovery to provide stimulating presentation of the data, many business users or analysts will not be inclined to use the tool. This is one reason I’m excited to see the integration with Excel and visualization toolsets; as Splunk can roll out these tools, it should be able to grow share with business users. Splunk has made it easy to access the tool with a free download on their website.

My recommendation for clients already using Splunk is to look at the expanded use cases above and see if any of these might apply to your organization. As you build your business case, don’t go to business managers (especially high-level ones) and show them a bunch of machine data. Splunk is definitely able to apply search and analytics on large volumes of data for what my colleague refers to them as big data machine for operational intelligence. Give them the right type of analytics and metrics and show them how Splunk helps solve issues such as the 360-degree view of the customer. For those currently without Splunk, there are many use cases including systems management and security along with customer interactions and experience in which it provides value and can combine systems and business data together dynamically.

Regards,

Tony Cosentino

VP and Research Director

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