Posts Tagged ‘data strategy’

Does Your Firm Suffer from Information Disadvantage?

In many of our client assignments, we’ve been challenged to solve significant information gaps. Managers understood that they were significantly behind their competitors in leveraging information, but they didn’t really know where to start in evaluating their deficiencies.

We came up with a scorecard methodology to evaluate 5 key areas that make up an information platform. We approached the evaluations party on how well the information platform was built, but also partly on whether what was being built and used was aligned strategically with the goals of the organization. The 5 key areas are:

Info Disadvantage - Chart

1) Information Capture: how information gets captured or procured. Is the firm capturing the right internal information from their systems or personnel, and are they procuring the right third party data?

2) Data structuring and integration: how well the data is organized, structured, and integrated with other information within the firm. Are the data from different sources brought together to complement each other and matched so that similar information is well mapped and accessed together (for example information about a certain client)?

3) Data availability: making the data made available for review and analysis. Are tools built or made available for analysts and data scientists to explore the data?

4) Information context: enriching data and creating metadata to give more meaningful cuts and views of information that is more relevant to managers. This is related to the data structuring  point above, but differs in that much of this context is business-specific and in many cases need to be defined by the business, specifically product and sales managers. Are there consistent and global definitions in order to make sense of the information, for example consistent definition of products and regions?

5) Information delivery: creating tools for managers and salespeople to access, receive, and interact with their information so they can use it to enhance their business. Are these tools easy to use, intuitive, fast, and up-to-date.


What’s interesting is to note how many of the client’s past projects have identified and attempted to expensively address deficiencies in one or two of the five key areas but have often rendered sub-optimal results because problems in other areas were not addressed. In a lot of instances, for example, large investments have been made in information delivery, creating front-end tools to dress up information, but lesser investment in cleaning up the structure of the underlying data or not procuring 3rd party data that could add significant value to existing data.

Firms need to strive to realistically evaluate their platforms in all five fundamental areas and make sure that they understand, and invest strategically, in each of those areas. The knee-jerk response by managers is to argue that it would be prohibitively expensive to address the build-out of the entire platform. Our response is that you don’t have to build out all the areas in full and and they don’t have to be done at the same time. If you target certain specific deliverables that involve development across all five areas, you approach this problem more cohesively and inter-dependencies become much clearer. Also this will most likely be a multi-year project, so it should be planned and invested as such. The project also should be designed to include an element of flexibility to allow for evolution of requirements as the business changes over time.

With this broader approach, an organization can ensure that projects they embark with will be more successful and valuable to the organization.

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Velocity of Information

Photo Aug 28, 5 26 15 PM (2)An interesting concern that we often have to address is the worry that manager and salespeople risk being overwhelmed with information. Most people can barely keep up with their existing information flow. What would new screens and automated emails do to their inboxes?

We discourage people from looking at information as a collection of valuable pieces of data that have to be stacked and reviewed, like a stack of magazines. We think it more like a river of information that doesn’t need to be read every time it updates, but rather the flow increases the perceived proximity to the most current information.

Proximity of Information

“Proximity” is the operative word. Decision-makers at all levels need to feel that the information they need is close and easy to acquire. This could translate to several different delivery scenarios depending on factors such as sophistication of the userbase, technology available, volume and rate of change of the information.

The analogy that we like to use is that information is the lifeblood of any dynamic organization, so if information flows at the pace of molasses, the organization will stagnate. What’s interesting about this perspective is that we are actually striving to reduce the value of a single given piece of data crossing someone’s desk. An email with a snapshot of revenue information that is only made available once a month, for example, is going to be held at higher value relative to a weekly or daily revenue alert with the same information.

Understanding Molasses

What holds information from flowing more quickly isn’t bad intentions, even though this tends to be an often cited cause.  It’s usually a combination of legacy behaviors ingrained in people’s habits and routines, as well as embedded hierarchical structures and legacy technology issues.

Because it’s a combination of things, not just one, it’s difficult to find one simple solution. By tackling only technology, for instance, and not addressing the other institutional obstacles, the promise of realizing higher information velocity may not be realized.

Getting everyone comfortable with the idea that what matters is increasing data flow is critical to dislodging information and weakening the molasses.

People need to feel that they can easily dip into the information flow naturally and not stress about “missing” or “losing” critical data points. Increasing information “velocity” increases the ease in which people interact with knowledge within their firm.

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Big Data: What’s the Hadoopla all about?

Big DataThe latest buzzword dominating business headlines today is “Big Data”. What was once known in past incarnations as Data Processing, shifted to become Data Mining, then transforming into Business Intelligence, and now has finally adopted the name “Big Data”. It’s still unclear to many business managers what Big Data is. A lot of technology managers have a hard time explaining it. All this makes it hard to make the value it promises tangible enough to invest in. The core of the problem is that “Big Data” isn’t just one thing. It’s an umbrella category for a series of technologies and methodologies that have made dramatic strides in innovation and business application, with a core goal of leveraging data to create dramatic and sustainable competitive advantages.[more…]

I’ll set a baseline definition for each of the four main categories implied by the term “Big Data”: data sourcing; data storage and staging; analytics and insight creation; new business models. This should give a non-technology manager a basic understanding that could get them through a cocktail party.

Data Sourcing

This is simply identifying, or in some cases creating, sources of information that can provide, alone or integrated with other data, new insights for a business. New technologies have emerged that make it much easier to source information that wasn’t easy to capture before, like customer interactions. Other technologies have created new fountains of data that businesses can tap into to gain additional insights, such as Twitter and GPS location information. The gap between what the optimal or “dream” data universe has to look like to create a sustainable competitive advantage versus what is currently available to a business is quickly closing. Managers should think big about what information would be game-changers and challenge their technology groups to find it and capture it. In previous posts I’ve recommend a book by Douglas Hubbard called How to Measure Anything which provides a fantastic framework.

Data Storage & Staging

A radical drop in the cost of storage has made the warehousing of large amounts of data a manageable economic proposition for most firms. We now talk about capturing and storing terabytes and petabytes of information. More importantly, new technologies, like the much hyped Hadoop framework, can pull data from clusters of data servers in an extremely efficient manner which solves two major headaches: 1) stored data can be quickly extracted for analysis, in some cases near real-time, and 2) fragmented data from multiple silos can be combined and analyzed together.  Firms like Cloudera and MapR provide cloud software and services that can help in developing the right structure. 

This new kind of infrastructure allows data to reside in large distributed networks that provide speed, perpetual access, and data redundancy, and has been labeled Cloud computing. Cloud services are offered by a growing group of firms that provide simple and scalable services. Two of the best known are Amazon and Rackspace.

Analytics & Insight Creation

What analytics you want to create largely depends on what data you have and what form it takes. An important feature of “Big Data” analytics is its flexibility with both structured and unstructured data. Traditional structured data is organized within set fields and in relational databases, while unstructured and semi-structured data require additional enrichment and organization to allow them to be useful for analysis and insight generation. An experienced analyst, for example, can create algorithms that ferret out useful items found in written text documents, then match them to other relevant data.

There are a variety of analytical tools in the market, most of them very rich with features. The level of sophistication of the tools, and subsequent modeling, used in any analysis depends on the complexity and volume of the data, as well as the kind of insights that are being sought. A common mistake is to procure overly complicated tools and build overly complicated models that don’t improve business outcomes. Managers need to define very clearly up front what kinds of results they want to get.

SAS and R are the standard-bearers for statistical analysis, and the go-to tools when you are looking to find correlations and testing causation in your data. The biggest bang, though, comes from an ability to create visualizations of your analysis, making it easier to digest, remember, communicate, and inspire action. A variety of vendors, such as Spotfire, Tableau, and QlikView, provide visually-driven Business Intelligence software that can be used to create intuitive analysis and dashboards used by managers or salespeople for decision making.

New Business Models

Putting a “Big Data” infrastructure in place at a firm won’t automatically ensure a return on investment. Businesses need to change in order to incorporate Big Data insights into their DNA. In some instances, completely new businesses models have emerged, like in the cases of Amazon and Netflix. In existing businesses though, a new data discipline needs to be put in place. It starts with management articulating clear goals and measures of success, followed by planning and execution of a Big Data plan that is in-line with company culture and talent, and lastly developing incentives and training that promote data reliance.


Despite the hype, Big Data really isn’t anything new. There’s been a renewed interest in the four principal areas that comprise it due to new innovations and impressive results of businesses that have fully adopted it. There is a tremendous opportunity to realize value using these tools, so it’s important to have a strong working knowledge of what they are and have it in the back of your mind as you think about your business strategy.

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