Is the era of the end to end solution provider over?

Over the years, many IT firms have built large product portfolios around the idea that diversity and breadth of products is critical to growth and effectively enabling customers.  These firms, including HP, IBM and Dell, have refereed to themselves in many terms, but all roughly mean “end to end solution provider” because of their ability to delivery all necessary technology from the clients desk, through the required software, network connectivity and systems for the data center including storage, servers and backup devices.  This portfolio diversity has mainly come from firms acquiring one another.

In recent years, companies such as IBM and HP and sold off or split up large assets to become more nimble and focus on a narrower portion of the market.  The last major firm that has a complete portfolio, which could be characterized as end to end is Dell.  The reason for splitting the portfolio, in the case of IBM was to focus on their core business of services and software.  For HP it was to enable the client focused business to operate on its own, separate from the enterprise business which each have distinct buyers, buying patterns and industry trends.

This trend is not unique to IT either.  Many firms in manufacturing, power generation and transportation have followed a similar path over the years; developing a large portfolio of assets, only to separate out  for simplification and to enable better focus of a core business.  This can be seen across Siemens, Rolls-Royce and GE.

Why it is hard to be good at everything?

As a company, any time you diversify away from a single product, no mater how connected or interrelated the product sets are, it means executives at all levels have to make priority calls.  Just because software requires connectivity to operate does not mean there is an inherent advantage in having both software and switches in the product portfolio. These priority calls are what large companies struggle with, because a decision to provide resources to one project is by default, a decision to not invest in another.  There is a finite amount of resources for companies to apply across the product portfolio, and these resources including financial, staff, knowledge, experience, and executive support can only be sliced into so many individual pieces before parts of a diverse portfolio begin to unravel and suffer due to lack of investment.

The other struggle is vertical expertise versus horizontal capability.  Many smaller players are successful because they have deep knowledge of a specific industry, this enables them to carefully plan and develop features and workflows that meet expectations that are unique to specific markets.  This is difficult to accomplish when you are managing a large portfolio because of the differing needs across storage, servers, software, services, and networking and client devices.

Acquired assets present a unique challenge when working to integrate components across the stack and create a unified look and feel that is unique to specific companies.  Every acquired company has different development standards, different programming languages and different types of legacy customers that must be supported.  This causes many acquisitions to struggle to hit their intended value targets because of technology baggage and lack of additive value across the portfolio.

Integration is part of every project, why not pick best of breed?

Today, IT projects are a complex maze of technologies that are strung together by different generations of technologies, varying business processes and evolving industry requirements for compliance.  Because of this diversity in customer requirements and existing systems and processes that must be accommodated, it is difficult for any company to truly build an end to end solution that meets all the needs of the business as well as IT operations teams.

Almost every IT project has a component of integration between components from different vendors.  This integration is often done by outside consulting and services teams, enabling an organization to focus on their core business and long-term operations.  This model enables organizations to quickly deploy new, complex technologies, but ensure they have the backstop and support of a consulting team to assist with the complex integration and the experience that requires, but is hard to develop in-house.  Outside consulting teams bring a wide range of experience at complex projects from other clients, enabling an organization to leverage that expertise to move more quickly and avoid known pitfalls for deployment of complex systems or integration with legacy platforms.

Does this mean there is no place for end to end providers?

I believe that successful technology firms will fall into one of two categories:

1. Services Focused – Companies that focus on being vendor neutral and enabling customers to rapidly deploy complex solutions that are aligned with business needs.  These firms will develop their own intellectual property through methodologies and tools to speed delivery and lower risk to project deliverables.
2. Specialized Product Focused – Firms that are focused on delivering a small number of products, with specific uses and touch points within an enterprise.  These firms will develop partnerships for technologies they integrate with; too ensure that components are certified and work together, lowering risk to implementations.

Big Data Search

Search is the most common method used today to locate information, present it and interact with complex data sets.  Search has brought access to large, complex data sets to the masses; enabling people to locate information that previously would have been difficult or impossible to find.  In the context of Big Data, we hear search mentioned a lot.  It can be referenced in one of two ways:

• Search as an interface – Search as an interface allows people to use a single entry point to reference complex data sets, the components that make them up and the relationships between pieces of data.  Search provides the front user interface to a dataset that is analyzed and manipulated by other tools.
• Search as product – Search as a product is the packaged components need to index data from a variety of sources and present the results through a search interface.  Search as an interface commonly allows for key word searches as well as phrases.

Search can provide a powerful interface to locate data and present it, but the search engine still needs the data presented to it in an organized fashion.  This is where additional analytical tools come in and enable rich, more powerful search results to be provided.

Companies like IBM, HP, and Microsoft have long provided search as a product, these tools bring in data sets, index it and enable folks to search for key words or phrases.  In July 2013, Cloudera also announced this capability on top of Hadoop.  The announcement from Cloudera was their inclusion of SolrCloud on top of Hadoop to create highly scalable search indexes.  This search capability allows customers to locate information quickly, but the customer must know what they are looking for.

Kitenga enables a richer search experience though its ability to extract entities from unstructured data, analyze the data from that extraction process, presenting not only key word results, but relationships and contextual meaning for the data that was analyzed.  Kitenga leverages search as an interface to access these complex data sets and the relationships derived from them.  Kitenga enables an end to end analytical pipeline of data analysis, relationship identification, and presentation of the results through search.

How do we determine the ‘value’ of data?

Data is rapidly becoming a new form of currency – It is bought, sold and protected in ways similar to currency and the commodities that back it.  There are a variety of examples of companies that have been built around the idea of collecting and using data in creative ways, enabling business models not previously possible.  Facebook, Google, Yelp, Groupon and a host of other companies across many industries have embraced this model of using data in new ways to create new business models.

But how do we value data?  How do we assign an exchange rate to something that is very much valued in the eye of the beholder?

Value takes many forms.  Using a financial metric is one way, assigning a price, or the impact to the business.  But ultimately value is set by the influence an item has on the world around it.  When it comes to data, the value has three components:

1. The cost of gathering the data
2. The cost to replace any lost data
3. The potential for future gains because of the insight gained from the data

Data is rapidly becoming the most important commodity of all.  Companies buy it, trade it, sell it, insure it and use it as leverage in negotiations.  Data has rapidly eclipsed the traditional, tangible assets of business as how a company is valued on the market and to its customers.

Many successful companies today work to ensure that have a large customer base to pull from; they leverage that base to collect as many details as possible on customers’ habits, motives and behaviors; and in turn companies use that information to grow their markets, their attach rates and their business.

As the worlds’ economy continues to shift towards services and intellectual offerings, and away from traditional manufacturing, data will only become more valuable. Over time more and more companies will have to ensure they have proper corporate policies in place to track and protect the data that drives the organization and ensure they can maintain a competitive advantage.

Big Data, Analytics and Hadoop

 Three of the biggest catch terms in technology today are Big Data, Hadoop and Analytics.  Often times, they are used interchangeably, when in reality they are three unique capabilities and categories.  Each solves a different, but related, set of struggles within IT and the businesses that leverage IT for unique advantages.

Start with Big Data.  Big Data is a term we hear more and more often about the developing struggles at many companies to cope with growing volumes of data and changing varieties of data.  There is no mark that says where Big Data begins, but is rather a dramatic change for an organization that requires them to look at new tools, technology, processes and skill sets to address the challenges they face.  Big Data is focused on the efficient storage, movement and organization of these evolving data types.

Analytics is related to Big Data, but has some variations.  Where as Big Data focuses on the data itself and the infrastructure to manage the data, Analytics is about putting that data to use and enhancing the capabilities of an organization.  Analytics is about taking that data and ensuring that actionable information comes from it that the company can execute on.

Finally, Hadoop, one of the most common technologies being talked about today.  Hadoop is a technology that can be used to enable organizations to manage their Big Data, while building a platform for more advanced analytic capabilities.  Hadoop is a rapidly growing open source project that has been adopted by many main stream organizations as a standard method for the storage and processing of complex data sets.  Hadoop is one of many tools that you can use to enable Big Data and begin to implement an Analytics strategy.