Tag: big data

Analytics Services Market Maturing Quickly | Sherpas in Blue Shirts

Analytics has been a bright spot in the services world, particularly for the Indian service providers as their analytics practices have grown faster than the rest of their organizations. They often are able to command premium pricing in this space, and it holds the tantalizing promise of transforming other service lines such as ITO, apps dev, and BPO. However, I’m making a bold prediction: The analytics practices are going to quickly hit maturity and the rate of growth will quickly slow.

We at Everest Group observe three maturity characteristics now happening in this space, so the “recipe ingredients” are in place for this market to start maturing.

  1. Gold rush stage. As companies come to understand and believe in the power of analytics, they are eager to do proofs of concept, which they then scale into a Center of Excellence (CoE). For the most part, the leading providers that offer analytics services establish a CoE or complement an existing CoE with data scientists. But data scientists are scarce, so they often use partners to augment their CoE. But the analytics gold rush is starting to ebb. Many providers have already seen the light and are already on the journey to establish or scale up a CoE. Therefore, the market will mature.
  2. Analytics becomes core. At Everest Group, we see a trend in which the benefits of analytics are so strong that analytics customers over time tend to want to build their own CoE and use their own capabilities, leveraging third parties only as an overflow or extension of what they are doing.With the return on investment in analytics being so high and customers viewing analytics as core or necessary to their business and competitive advantage, they view the expense of building an internal analytics CoE as a justifiable cost and wise decision. Therefore, service providers’ labor arbitrage offerings are less compelling.
  3. Benefit doesn’t pull through to a process. For the service providers that have built a capability around analytics, it should lead to complementing other BPO or IT practices; but we have not seen this as a common occurrence. We believe the reason is that the customers’ stakeholders block providers’ access and seal them off translating the analytics work to a broader business process or IT application. The providers’ hope of pulling through work has not manifested consistently in a large degree.

As we analyze this issue, we believe there are three areas where analytics providers can build distinctiveness:

  • Provide access to proprietary data
  • Build proprietary tools
  • Provide capability

As already explained, we expect the market for providers whose practices are built on capability will slow rapidly. But we see substantial opportunity where a provider combines proprietary data and proprietary tools with capability that focuses on a specific business problem.

An example of a scaled analytics program that has achieved billions of dollars in this way is OptumRx. This solution includes a proprietary data source, proprietary tools and capability focused on a business problem that serves the healthcare industry at scale. And it generates billions – not millions – in revenue.

We believe that providers that transition to a model of creating proprietary data and customized tools combined with capability to solve a business problem will enjoy ongoing and potentially explosive growth.

But those that stay focused on providing capability and data scientists are doomed as they face a quickly maturing marketplace. It’s not that this space will go away; it’s just that it won’t grow fast and pricing pressure will start to take hold.

Although we believe the analytics market maturity will happen in the next two years, we think a lot of room and potential remains for providers that combine the three analytics components (data, tools and capability focused on a specific business problem).

Avoid the “Gotchas” in Purchasing Next-Gen Tech Services | Sherpas in Blue Shirts

The new technologies sweeping the market hold great promise of competitive advantages. But there’s a disturbing trend occurring in the services sales process for these technologies that poses a risk for buyers. Look out for providers talking about cloud, mobility, big data, the Internet of Things, and social in the same breath as SaaS/BPaas, automation, robotics, and artificial intelligence. Providers that jumble these technologies together as though they are homogeneous really don’t understand the implications of what they’re trying to sell you. They’re basically throwing mud against your wall and seeing what sticks.

The possibilities with all of these technologies are exciting, but they have distinctly different impacts on the buyer’s business.

As illustrated in the diagram below, we can bucket one class of impacts as those that create new business opportunities. They provide new types of services that enterprises can use to change the composition of their customers or provide different kinds of services. For example, the Internet of Things holds enormous promise around allowing enterprises to provide a completely different class of services to their customers. In mobility and social technologies, the digital revolution holds the promise of changing the way businesses interact with their end customers.

Changing technology opens up new opportunities but also creates strategic challenges

Changing technologies

The second class of new technologies (Saas/BPaaS, automation, robotics, and artificial intelligence) changes how services are delivered. For example, SaaS takes a functionality that was available but delivers it through a different mechanism. Automation and robotics changes the way service is provided by shifting from FTE-based models into an automated machine-based delivery vehicle.

The two buckets of technologies have different value propositions. The first class of technologies (cloud, mobility, big data, IoT, and social) are about getting new and different functionality. The impacts in the second class are lower costs and improved flexibility and agility. Each class of technologies has different objectives and value propositions and thus needs a different kind of business case. Buyers that mix these technologies together in a business case do themselves substantial disservice.

The way you need to evaluate the two distinct types of technologies (and providers offering them) is completely different. A provider that recognizes that automation, robotics, and SaaS are about changing the nature of delivery will have a much more thoughtful conversation with you and build its value proposition around flexibility, speed, and quality of service and cost.

A provider that recognizes the impact of mobility, cloud, big data, and the IoT technologies will talk to you about a value proposition around standing up exciting new capabilities, creating new offers and changing the conversation with your end customers.

So, buyer beware. If you’re talking with a provider that mixes these technologies’ distinct value propositions together, you’re dealing with a provider that really doesn’t understand what they’re offering.


Photo credit: Flickr

Hadoop and OpenStack – Is the Sheen Really Wearing off? | Sherpas in Blue Shirts

Despite Hadoop’s and OpenStack’s adoption, our recent discussions with enterprises and technology providers revealed two prominent trends:

  1. Big Data will need more than a Hadoop: Along with NoSQL technologies, Hadoop has really taken the Big Data bull by the horns. Indications of a healthy ecosystem are apparent when you see that leading vendors such as MapR is witnessing a 100% booking growth, Cloudera is expecting to double itself, and Hortonworks is almost doubling itself. However, the large vendors that really drive the enterprise market/mindset and sell multiple BI products – such as IBM, Microsoft, and Teradata – acknowledge that Hadoop’s quantifiable impact is as of yet limited. Hadoop’s adoption continues on a project basis, rather than as a commitment toward improved business analytics. Broader enterprise class adoption remains muted, despite meaningful investments and technology vendors’ focus.

  2. OpenStack is difficult, and enterprises still don’t get it: OpenStack’s vision of making every datacenter a cloud is facing some hurdles. Most enterprises find it hard to develop OpenStack-based cloud themselves. While this helps cloud providers pitch their OpenStack offerings, adoption is far from enterprise class. The OpenStack foundation’s survey indicates that approximately 15 percent of organizations utilizing OpenStack are outside the typical ICT industry or academia. Moreover, even cloud service providers, unless really dedicated to the OpenStack cause, are reluctant to meaningfully invest in it. Although most have an OpenStack offering or are planning to launch one, their willingness to push it to clients is subdued.

Why is this happening?

It’s easy to blame these challenges on open source and contributors’ lack of coherent strategy or vision. However, that just simplifies the problem. Both Hadoop and OpenStack suffer from lack of needed skills and applicability. For example, a few enterprises and vendors believe that Hadoop needs to become more “consumerized” to enable people with limited knowledge of coding, querying, or data manipulation to work with it. The current esoteric adoption is driving these users away. The fundamental promise of new-age technologies making consumption easier is being defeated. Despite Hortonworks’ noble (and questioned) attempt to create an “OpenStack type” alliance in Open Data Platform, things have not moved smoothly. While Apache Spark promises to improve Hadoop consumerization with fast processing and simple programming, only time will tell.

OpenStack continues to struggle with a “too tough to deploy” perception within enterprises. Beyond this, there are commercial reasons for the challenges OpenStack is witnessing. Though there are OpenStack-only cloud providers (e.g., Blue Box and Mirantis), most other cloud service providers we have spoken with are half-heartedly willing to develop and sell OpenStack-based cloud services. Cloud providers that have offerings across technologies (such as BMC, CloudStack, OpenStack, and VMware) believe they have to create sales incentives and possibly hire different engineering talent to create cloud services for OpenStack. Many of them believe this is not worth the risk, as they can acquire an “OpenStack-only” cloud provider if real demand arises (as I write the news has arrived that IBM is acquiring Blue Box and Cisco is acquiring Piston Cloud).

Now what?

The success of both Hadoop and OpenStack will depend on simplification in development, implementation, and usage. Hadoop’s challenges lie both in the way enterprises adopt it and in the technology itself. Targeting a complex problem is a de facto approach for most enterprises, without realizing that it takes time to get the data clearances from business. This impacts business’ perception about the value Hadoop can bring in. Hadoop’s success will depend not on point solutions developed to store and crunch data, but on the entire value chain of data creation and consumption. The entire process needs to be simplified for more enterprises to adopt it. Hadoop and the key vendors need to move beyond Web 2.0 obsession to focus on other enterprises. With the increasing focus on real-time technologies, Hadoop should get a further leg up. However, it needs to provide more integration with existing enterprise investments, rather than becoming a silo. While in its infancy, the concept of “Enterprise Data Hub” is something to note, wherein the entire value chain of Big Data-related technologies integrate together to deliver the needed service.

As for OpenStack, enterprises do not like that they currently require too much external support to adopt it in their internal clouds. If the drop in investments is any indication, this will not take OpenStack very far. Cloud providers want the enterprises to consume OpenStack-based cloud services. However, enterprises really want to understand the technology to which they are making a long-term commitment, and are cautious of anything that requires significant reskill or has the potential to become a bottleneck in their standardization initiatives. OpenStack must address these challenges. Though most enterprise technologies are tough to consume, the market is definitely moving toward easier deployments and upgrades. Therefore, to really make OpenStack an enterprise-grade offering, its deployment, professional support, knowledge management, and requisite skills must be simplified.

What do you think about Hadoop and OpenStack? Feel free to reach out to me on [email protected].


Photo credit: Flickr

IBM Takes Steps to Ensure It Will Be Relevant for the Future | Sherpas in Blue Shirts

IBM is taking some bitter medicine right now in its series of divestments. Big Blue recently exited the chip manufacturing business by spinning off that division to Globalfoundries. The move comes on the heels of having exited its server business and voice and transaction BPO business. There’s a lot of media attention to “IBM’s blues” and a lot of water cooler talk about what IBM is up to. Are they going to be viable, or do they have a foot in the grave? I look at it as they are ensuring that they have both feet on a very solid growth platform.

But the series of divestments raise a lot of eyebrows and create shareholder discomfort. It takes time for shareholders and customers to process what IBM is doing.

Here’s what’s happening:

  • The divestments were not failed businesses. They just are not the future of IBM. The company is simply pruning back its operations that have been a drag on earnings and siphoning their management attention and resources.
  • IBM has been acquiring almost one company per month, largely in the areas of cloud software, analytics and Big Data.

Often the assets IBM sells do well in other hands. Lenovo has done very well with IBM’s former PC business and looks to do well in the server business. And I expect Globalfoundries to do well with the chip business.

Simply put, IBM is remaking itself and making very deliberate and assured steps for its future. It is rare for large organizations to have the discipline to exit businesses. Most large organizations are eager to buy new growing businesses but struggle in the divestment of businesses that are no longer strategic or are struggling to perform. But IBM has managed to remake itself a number of times in their long, historic journey.

IBM now clearly has both feet in the future, whether it’s a growth platform for cloud, analytics, or high-value IT and BPO services.

I think this should be a comfort to IBM customers. Big Blue is taking necessary steps now to not become a Kodak and not consign itself to irrelevance for customers’ future needs.

Analytics Consumerization: Think “data doctor + tools = data scientist?” Think again | Sherpas in Blue Shirts

In October 2012, the Harvard Business Review named “Data Scientist” the “sexiest job of 21st century.” While this profession has since gained meaningful acceptance and understanding in the broader big data analytics world, the dearth of real data scientists (some believe there are only 3,000 in the world), has opened the door to what I call “data doctors.” And many enterprises desperate to make sense of their burgeoning data to drive business value might get duped by IT consultants or aspiring candidates masquerading as data scientists.

What’s the distinction between the two? Data doctors – business intelligence analysts, ETL developers, data assemblers, data quality testers, data analyst, etc – are skilled in working with data. But data scientists typically deal with complex algorithms and statistics to unearth the hidden treasure in big data. They give yet unexplored meaning to the data. Their work has a higher degree of risk and probability to fail, but it also delivers the highest rewards. They are the ones responsible for big-ticket transformational ideas.

Yet, with the increasing consumerization of analytics and the realization that the data scientist pool is minute, many enterprises believe they do not need data scientists as:

  1. There may not be all that much value in the data
  2. Data scientists are very expensive
  3. When given the right tools, data doctors can replace data scientists.

While it’s true that data scientists are expensive, the other two above points are erroneous. There is a lot of value in data that data doctors are unable to mine. And assuming that a college graduate or an IT engineer adept in BI technologies can become or substitute for a data scientist by leveraging new age big data solutions is a mistake.

These “consumer focused” solutions hide the complexities of generating meaningful insights, data discovery, and visualizations by adopting a WYSIWYG (What-You-See-Is-What-You-Get) approach where users can assemble workflows and analytics logic/model using drag and drop in a highly intuitive user interface. These technologies are destroying the data custodian ivory towers of corporate IT, and making business analysts perform substantial analytics projects on their own. But make no mistake… they do not reshape analysts, the data doctors, as data scientists.

While it is true that new age technologies help data and business analysts skill up to perform more advanced analytics, assuming this eliminates the need for data scientists is akin to saying we don’t need human pilots due to the auto pilot function. Indeed, the great demand for real data scientists is the reason many universities have launched dedicated data scientist programs (e.g., advanced analytics programs at Columbia’s Institute for Data Sciences and Engineering).

The real value of these new analytics tools is in enabling data doctors to perform many tasks previously handled by data scientists, thereby freeing the prized scientists to work on resolutions to highly complex problems that can significantly benefit the business. They also help enterprises who believe data scientists are overkill to enable data doctors to perform reasonably complex tasks.

However, enterprises really interested in data-driven insights will be best served by empowering both scientists and doctors. The doctors need to keep updating their knowledge about the latest analytics solutions that can help them add more strategic value. And data scientists need to dive deep, unravel unexplored territories, and develop data-driven insights that can transcend the boundary of human intelligence.


Photo credit: Stephen Coles

Service Providers Struggle Using Big Data and Analytics to Drive Growth | Sherpas in Blue Shirts

Observing service providers’ much talked about efforts to provide new levels of value and create new growth opportunities through big data and analytics reminds me of a quote often attributed to Yogi Berra, the great NY Yankees coach. “In theory it’s simple, in practice it isn’t.”

Yogi captures, as only he can, the timeless truth that sometimes things that are obvious and easy to articulate are very hard to execute. Those of you who play golf will immediately recognize the power of this observation.  The service industry’s collective experience with big data and analytics is causing a lot of service providers to also identify with this Yogism.

Service providers have been quick to recognize the potential for big data and analytics to change the game in their increasingly commoditized offerings and have made substantial investments in talent and tools they hope will be important in applying these new sources of value to their customers. In a cursory analysis these efforts are encouraging and yielding fruit. The providers continually talk about it to customers and crow about the rapid growth they are seeing in these areas.

But upon further reflection we see mostly a set of tools and capabilities with a lot of hype but disappointing total revenue given the amount of attention and hype. We find that the growth comes off a very small base and amounts to a small total revenue.

The use cases the providers use as examples are few, and they use the same use cases or case studies again and again. Plus the case studies are very industry and company specific and therefore are not easily repeated across the customer base. Basically these are one-off solutions that don’t lend themselves to broader industry application.

Big data and analytics are powerful and obvious in terms of their impact. It’s simple in theory. But in practice it’s difficult to build large big data and analytics revenue streams.

Frenemies IBM and Apple Team Up to Shake Up the Enterprise Mobility Space | Sherpas in Blue Shirts

On 15 July, 2014, IBM and Apple announced a sweeping enterprise mobility-focused partnership to create business apps and sell iPhones and iPads to Big Blue’s corporate customers, thereby bringing IBM’s big data and analytics capabilities to the iOS ecosystem. The venture includes more than 100 industry-specific enterprise solutions, including native apps developed for the iPhone and iPad, targeted at the retail, healthcare, banking, travel, telecommunications, and insurance verticals. IBM will leverage its 3,000 mobile experts and industry/domain consultants, to provide cloud services and onsite support for enterprises. The two companies will collaborate on IBM’s MobileFirst for iOS solutions, combining their distinctive strengths – IBM’s big data and analytics capabilities and Apple’s consumer experience and developer platform.

The Rationales Behind the Partnership

The intention of the deal for Apple is to enable its products to become go-to-offers for large enterprises. It also principally underlines the company’s immediate need to expand its presence in the enterprise world, as consumer sales peak and competitive intensity in its core market heightens. Meanwhile, IBM hopes Apple’s mojo can help revitalize its fortunes after nine consecutive quarters of year-on-year revenue decline, as it places its bets on mobility in the workplace. It will also help IBM solve its big data and analytics growth issues (i.e., providing Watson with much needed impetus through enhanced mobile users’ data), forming a pivotal part of a new growth story. (To this point…think back three decades to Apple’s iconic television commercial titled “1984,” when it attacked IBM as an evil Big Brother figure. Talk about a 180-degree turnaround!) iPhones and iPads are already owned by employees in large enterprises but are hard to manage and govern. IBM can leverage its enterprise-wide system management expertise to make a compelling value proposition, complementing its Fiberlink acquisition (a provider of cloud-based enterprise mobile management solutions). Additionally, it will help IBM cement its reputation as a leader in the “mobile first” movement in enterprise solutions.

Implications for Rivals

Microsoft will feel most uneasy about this alliance, as while its products are ubiquitous in corporate PCs, it has been a laggard in serving the mobile workforce. This is a critical whitespace its new chief, Satya Nadella, is determined to fix. Google, Samsung, and the Android bandwagon will also feel threatened, given their recent push in the enterprise market. To allay fears about Android’s security for enterprise use, Samsung has built a system called Knox into its devices. Last month at its developer conference, Google announced that it would embed software elements of Knox in the next version of Android. They will also have to look at alliances with other enterprise-focused vendors to shore up their business case. Also, if IBM becomes the de facto champion for iOS, it will have potential ramifications for other service providers such as Dell, HP, and CompuCom.

Multi-faceted Challenges

Apple has not targeted enterprises with any zeal in the past. Steve Jobs was infamous for his contempt for selling to enterprises, even referring to CIOs as chief information “orifices.” While the Tim Cook era has seen Apple making small but significant progress in courting corporate stakeholders, IBM’s significant experience in the space makes Apple/IBM a very unlikely pairing. Apple and IBM have drastically different people cultures. Any effective partnership will need to account for these differences. They also have very different go-to-market and channel strategies, which will result in friction over the direction the alliance takes. Their sales motions tend to be at odds, with IBM solutioning for a client, while Apple caters to essentially product categories. IBM has defocused severely from the end-user computing space. Does this alliance signal a revival in this regard? The companies’ divergent investment attitudes will make joint investments problematic. To complicate matters further, both have stark but strongly held philosophies about design, customer support, and sales, making collaboration painful. 

The Road Ahead

Partnerships and alliances such as this are notoriously difficult to manage. Both organizations will find it challenging to bring two entirely different culture sets to work cohesively as one. The alliance will need sustained resources, time, and senior leadership investments, along with a steadfast commitment to change management. Given the complicated dynamics sweeping the enterprise market, IBM and Apple have certainly stolen a march over rivals. We will need to keep an eye on the investments both are making into the alliance, the steps they are taking to mitigate the challenges, and the success stories that emerge as a result.

One thing is certain. The enterprise IT market is in for some interesting times. For further insight into the enterprise mobility space, check out our recently published viewpoint.

Social Analytics and an iPad to Chop Veggies | Sherpas in Blue Shirts

I recently watched a WhatsApp video in which a woman was visibly pleased when her advanced-age father said her gift of an iPad was “great,” then became baffled and shocked when she saw him using it as a vegetable cutting board!

While this is certainly an extreme example of something being used for a different purpose than its intent, we’re seeing the same type of disconnect with social media platforms and the associated analytics. Lots of organizations have deployed social analytics tool to assess the typical engagement metrics (e.g., number of users reached, time spent per user), beauty metrics (e.g., hashtagged or liked), or perspective metrics (e.g., positive or negative sentiments). Much like the iPad veggie chopper man, these enterprises believe the solution is doing its job well. However, like the daughter knew, this is not what social analytics platforms are made for.

Social analytics platforms should be deployed to generate value beyond tracking customer portal trawls. They are meant to listen to, engage, and amaze customers and prospects. However, very few organizations use them for those purposes. Hardly any of them have integrated social data with the main customer data bank. Moreover, there is little collaboration or coordination across social media, analytics, and sales teams, each instead working in its silo. Why is that? Although enterprises may give different excuses, I see four main reasons per my market interactions:

  1. Organizational challenges in terms of structure and complexity that no business manager wants to disrupt

  2. Lack of forceful evangelization

  3. Limited understanding of how to leverage social media and analytics

  4. Deployment of social media and analytics for “buzz purposes,” rather than as something meaningful

In various organizations, the entrenched old school senior management fundamentally does not believe in “new age toys” of social media. Many of them admit that social media is good to impress the CEO and tick mark their key performance indicators, but not good enough to drive meaningful business. This reluctance results in half-hearted strategies with little focus or commitment.

These reluctant organizations, however, have a very potent argument. They believe there are limited, if any, successful adoptions of analytics solutions that have resulted in revenue enhancement. While they think that analytics may help in running operations more efficiently, reducing costs, and enhancing their brand, they consider its direct impact on revenue to be weak.

Responsibility for this misperception falls both on technology providers and the buyers of analytics solutions, more with the providers. They publicize client adoption focusing on cost savings than revenue enablement. This diminishes the real value a business can derive from analytics adoption. And there are indeed organizations actively deploying social analytics to generate insights, serve the customer, and build the next product, many of which now have a Chief Data Officer overseeing the adoption of analytics solution.

How can an enterprise become truly social? Can it align the wide range of business units – including procurement, HR, finance, sales and marketing, product development, customer support, and quality management – to become social? Can it embed the philosophy behind social initiatives into its business processes? While the challenges are significant, this is where the value from social media initiatives lies. Silo-driven deployments will only add to the fragmentation, instead of helping the business.

Is your company using an iPad to chop its vegetables? Our readers would enjoy hearing your social media experiences.

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