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The role of marketing and competitive intelligence (M/CI) analyst seems to be constantly expanding. There is a never-ending stream of new data sources, rapidly changing markets, and constant disruption to keep on top of. But in reality, the way many companies approach CI is wasteful: in fact, our study found that over 75% of CI activities provide no value to companies.

The strategic importance of M/CI is clear to many companies, but there is often a cavernous knowledge gap between strategic intentions and the actual execution of any M/CI initiative.

A recent Forrester survey found 55% of M/CI professional struggle to make decisions informed by competitive intelligence.

This creates an organizational disconnect and inevitably leads to the M/CI function’s resources being cut when it fails to demonstrate value. If you want to conduct effective M/CI, it’s important to understand which tasks are a waste of time for an intelligence analyst. Often, you can use technology to automate these tasks and create better processes that free your analyst to focus on high-value work and use their true skills. 

Let’s be clear: by a waste of time, we don’t mean you shouldn’t do these tasks, just that, often there are better, more efficient ways to perform them.

The rapid acceleration in computer processing power and developments in AI means it’s easier than ever to wrap software around tasks that were previously manual, laborious processes. Often, this will result in dramatic performance improvements.

If you want to learn about making your CI function more efficient, read on. 

Here are the four things intelligence analysts do that are a waste of time:

1. Manually Collect Intelligence Data

It’s universally understood that robust data sets are the foundation of any intelligence initiative. The issue is that, in the internet age, there’s just an unfathomable amount of data out there. Cipher recently commissioned a survey by Forrester, and one finding was stark: 94% of intelligence professionals surveyed believed they spent too much time collecting data (Source: A commissioned study conducted by Forrester Consulting on behalf of Cipher March 2021).

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Needless to say, manually collecting this data is a fool’s errand. And yet, many M/CI practitioners still fall into this trap. Whether that’s manually downloading clinical trial data from clinicaltrials.gov, searching for the latest news about key competitors or managing an inbox full of Google Alerts, it’s easy to spend too much time focusing on intelligence gathering at the expense of time spent using analysis skills. 

By default, manually sourcing data severely handicaps any attempt to do comprehensive M/CI, because it limits the amount of data that can be included for analysis or the amount of time left for the analysis once data collection is done.  

Instead of manually collecting data, focus on a more programmatic approach. Use a software tool with built-in integrations to the data sources you need, and watch as the software platform automatically pulls in real-time data. By building strong, reliable data pipelines, you’ll be able to automate much of the time-consuming, low-value work associated with manual data collection. 

The payoff is huge: your intelligence analyst will be able to spend up to 45% more time on analysis. 

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2. Manually Organize & Tag Data

Once intelligence teams select their data sources and begin the process of collecting data, there’s still the challenge of cleaning, organizing, and tagging datasets before they can be incorporated into data analysis. 

Many analysts use traditional taxonomies to tag intelligence data within their systems. Taxonomy-based systems use hierarchical structures to tag and organize information - for example, a piece of content that contains the word “China” may be tagged as both “China” and “Asia”.

In practice, taxonomy based systems work well when a small number of people need access to your data and your data is static fitting neatly into pre-built categories. Often, intelligence analysts will be tasked with building, agreeing on, and managing these taxonomies: a task that’s agonizingly time-consuming. 

 

In practice, taxonomy systems do not work particularly well in the M/CI world, especially given the pace at which the world now moves. A better approach incorporates Natural Language Processing, or NLP, which is a subset of Machine Learning. The advanced data categorization capabilities of NLP-powered systems use AI and other machine learning algorithms to tag and classify data. They’re capable of filtering out irrelevant content, which dramatically improves the clarity of your data, and can identify new relationships and interesting data points that a human wouldn’t consider. 

All told, NLP systems are easier to set up and manage, more efficient at organizing data, and help intelligence analysts more effectively discover critical new insights. By moving away from a solely taxonomy-based approach to organizing and tagging data, intelligence analysts can gain back valuable time, and can also expect the data they include in their analysis to be better structured.     

3. Inefficient Vendor Management

Many intelligence analysts work with external vendors to access expertise that improves the efficacy of their M/CI efforts. In itself, this is not a waste of time: many third-party vendors add significant value to M/CI efforts. However, the process by which M/CI analysts engage external vendors is often riddled with inefficiencies. 

Common challenges include managing vendor progress, reviewing deliverables, and centralizing knowledge. But for many, the inefficiencies start earlier, and many intelligence analysts spend a lot of time working on scoping documents, Requests for Proposals (RFPs), Statements of Work (SOWs), and contracts. 

These are often repeatable processes, but intelligence analysts often have to go through the entire process manually, duplicating work that’s previously been done by someone else in the organization. By embracing a centralized intelligence hub that tracks previous performance, project deliverables, and other information, intelligent analysts can work far more efficiently with third-party vendors, greatly enhancing the effectiveness of external M/CI projects.

Related: How to Effectively Manage Competitive Intelligence Consulting Vendors


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4. Breaking Down Organizational Silos

For too many intelligence analysts, navigating internal politics and organizational silos is all too familiar a problem. Whether it’s finding important data, sharing learnings with other departments, or just working together on a joint project, many organizations--particularly enterprise, mature companies--are not set up in a way that makes this easy for intelligence analysts. 

Organizations nearly always possess the information that they need in order to do comprehensive M/CI, the problem often is that this information is scattered across different departments. Alysse Nockels, Director of CI at Tanium, recommends that organizations embark on a culture shift to overcome this. In her company, she breaks down organizational barriers with a simple rule: no department can consume information from intelligence analysts unless they contribute. 

Working towards a cultural change like this, that emphasizes CI as a combined responsibility of the company as a whole, can help eliminate wasted time for intelligence analysts. It also has the combined benefit of dramatically improving the performance of the entire organization, building an intelligent company. 

Knowledge360Ⓡ: A More Effective Way to Do CI

The majority of time drains for intelligence analysts can be solved by incorporating sophisticated software into CI workflows. The Knowledge360   platform is the world’s first intelligence hub: a one-stop shop for everything related to market and competitive intelligence within the organization.  

Knowledge360 has built-in data pipelines with a wide variety of data sources, and uses AI and NLP to automatically process and organize data. By automating these processes, organizations can free up their intelligence analysts to spend as much as 45% more time on analysis, which is where the real value from the CI process is derived. 

Knowledge360 now has built-in vendor management tools that make recruiting and working with vendors a smoother process for an intelligence analyst. By making the move to Knowledge360, organizations benefit from a centralized intelligence hub that significantly strengthens the performance of the intelligence function. 

To learn more about how Knowledge360 can save your intelligence analysts time, schedule a demo

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