5 Minute Read

Lately, I’ve been spending A LOT of time trying to find ways to explain what Knowledge360 does for potential customers. And I’ve found myself stuck between a rock and a hard place.

When trying to use terms like “competitive intelligence software,” we run into a whole host of problems. Namely:

  • Nobody really has a standard definition of what that is.
  • Every vendor that claims to have “competitive intelligence software” usually do things differently.
  • The term doesn’t even remotely begin to cover the full spectrum of what a tool like Knowledge360 is capable of.

I find the K360 platform to be a cross section of “market intelligence software” and “competitive intelligence software” with a little Knowledge Management thrown in to make things even more interesting. And while the term “market intelligence software” has many of the same problems as that of competitive intelligence software, most who see the platform agree we have features that happily live in both realms.

So what is a marketer to do? I could make up a category and words to use (which is really fun, but useless if you’re the only one using them). I can also look to other industries to help give people a frame of reference or introduce them to something that is similar.

This article is meant to offer a similar example in an adjacent industry where some of the same struggles caused very clear and specific terms to emerge. In this case, I am going to take a look at the world of databases (scintillating, I know).

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Data Lakes vs Data Warehouses

One of the newer trends to emerge in the database industry places offerings in one of two separate categories: data lakes and data warehouses.

Data lake: a data lake is basically storage of a lots of data, of varying types and structures, which has yet to have a defined use or purpose.

Data warehouse: a data warehouse is, on the other hand, a collection of structured and filtered data that has been processed and prepared for a specific use.

Data lakes and data warehouses are both very important, and each has it's ideal use. Data lakes are incredibly easy to add data to, and useful for data scientists who make use of raw data. It is also the best format of data from which to use machine learning or big data analytics to glean insights from data.

Data warehouses are more useful to business professionals who have a very specific purpose for the data they need to analyze, where that data has been cleaned, normalized, and formatted for their specific purposes.

Many companies who deal in data will have both data lakes and data warehouses for different purposes. Data lakes are great for storing large amounts of data for multiple purposes, and this data may then be filtered into multiple data warehouses. As it moves between the two, the data will be organized, normalized and prepared for the specific use the data warehouse will serve.

Why does this matter in the context of Knowledge360? Let me tell you...

Intelligence Lake to Intelligence Warehouses

Knowledge360 is, in essence, a combination of a data lake and multiple data warehouses, all of which are built specifically to house competitive intelligence and market intelligence data, information, knowledge, and wisdom.

Important to this discussion are the definitions of data, information, knowledge, and wisdom. You can read the full article breaking down their differences if you like, but for the purposes of this article, our definitions are:

  • Data is raw, unanalyzed, unorganized material that is the result of observing events, environments, and ourselves by our senses and modern sensors.
  • Information is the set of data that has already been processed, analyzed, and structured in a meaningful way to become useful, and usually represents patterns that can be recognized from data.
  • Experience and intuition lead to knowledge, which makes sense of information within the context or application surrounding that information.
  • Wisdom represents human beliefs, purposes, values and judgment which allows us to make decisions based on knowledge.

This graphic depicts the way that data comes in, is transformed into information, and used to build an organizational knowledgebase from which strategic decisions can be made.

 

Researchers collecting M/CI to spot trends

Click to Enlarge Image

Step 1: Identifying and Collecting Your Data

The first step in the process is identifying the inputs of data from your various known sources. This may include external news sources, field intelligence from your sales team, web monitoring data about your competitors, or even third-party market research projects. 

Don’t worry about being too specific, especially when you are collecting data on a new company or topic. Remember, your data is going into a lake, not a warehouse. While your less defined search criteria will deliver more “noise” than your narrowly defined search, the broader search is more likely to uncover previously unknown information or identify outliers you may not have seen. 

This ingestion of data covers data that fits into both competitive intelligence and market intelligence categories.

Step 2: Storage in the “Intelligence Lake”

Up next, all of this data is stored in its native and unaltered format in what I affectionately refer to as the “intelligence lake.” Simply put, you have copied the data you identified and collected it in a single repository. The data you have is unaltered and exists in its original format. Your intelligence lake may contain news articles, PDF documents, RSS feeds, and all types of different data, much like a data lake.


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Step 3: Transforming Data into Information

Up next, we see the transformation from raw data (which by itself is of little use) to information (which is now something we can work with). Both through machine and human-based actions, it’s time to make sense of the data in the “intelligence lake.”

Those using a CI tool leveraging recent advances in natural language processing and machine learning algorithms (which, as I mentioned earlier, are very helpful when applied on a collection of different data structures), automatically tag and organize their intelligence data. With these low-value tasks completed by technology, your analysts are able to perform the analysis only possible by humans, like connecting consumer actions to identify buying trends, or comparing shared data between you and your competitors.

This now creates information—data that has been made useful and structured in meaningful ways for you and your organization.

Step 4: Creating Value with Intelligence Warehouses (Dashboards)

Now that we have organized information, it is time to put it to work. I believe that presenting curated information, with or without human analysis, is best done with dashboards. 

The most successful market and competitive intelligence (M/CI) teams build dashboards for each specific purpose. You may have dashboards focused by topic area, or built specifically for a business unit. 

Dashboards may repeat sections but include different associated information. For example, your VP of Sales and CMO may both be interested in the latest product enhancements a competitor has made, but the CMO probably does not need to see geographic hiring trends in your competitor’s salesforce, just as your VP of Sales doesn’t need to see your quarterly estimates for your competitor's digital ad spend.  

Base your dashboards on what each audience needs to know at a glance.

The dashboards you build are conceptually similar to individualized data warehouses, in that they are made uniquely for the audiences who use them. Think of your dashboards as little “intelligence warehouses,” each made for a very specific use.

The fourth step is where meaningful insights are formed based on intuition, experience and timing. It’s time for your newly formed knowledge to be disseminated across the organization.

Step 5: Strategic Execution

This is where the magic happens. Everything else has been foundational, built in service of this final step.

It’s time for the organization to take strategic action or make informed decisions. Following the five step process I’ve outlined, or another similar process, will position the Intelligence Lakes and Intelligence Warehouses you and your team need to help determine how your business will approach a new market, launch a new product, position a service, and so much more. 

Delivering Key Benefits

You operate in a competitive market so making the most of your resources is key to delivering the greatest value to your organization. The advantages you had yesterday will quickly become table stakes in tomorrow’s market. Reducing the amount of low-value work performed by your high-value team members is critical to unlocking these benefits. 

Selecting the right tool allows your market intelligence, competitive intelligence or research team to spend the majority of their time turning information into useful knowledge and insights, rather than collecting and organizing data into information (which I guarantee is where they spend it now).

Leveraging technology has the potential to act as a force multiplier, expanding your capacity and positioning you to deliver your organization the right knowledge at any given time to make the best strategic decisions possible. Without it your work will likely remain reactive, forcing you to drop everything and dive in to research the impact of a surprise event (which I guarantee is what happens now).

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