Strategic objectives, data analysis, investment intelligence – to small entrepreneurs with no background in economics or business management, these new-age concepts sound intimidating. Don’t worry, though – it’s all business as usual. At least the way big players are playing it.
Be they big or small – all ambitious businesses must rely on data.
The rise of the internet and the climactic peak of the information age have changed the game for good. In this new climate, intelligence truly is the most valuable asset. Success and failure are determined by an organization’s ability to collect, analyze, and apply large datasets. Investment intelligence is much more than social media listening; it’s about minimizing risk and making infallible business decisions.
Here’s some wisdom from experienced investment intelligence analysts:
Table of Contents
What is Investment Intelligence?
Traditionally, investment intelligence is reserved for VCs, hedge funds, and other financial firms. It might sound like one, but it’s not a new-age business philosophy. It’s a long-established approach to making smart business investments, the one they teach in schools. Investment intelligence is the use of secondary data for identifying and evaluating investments. Secondary data refers to alternative data sources that exist outside of the organization.
As a strategic approach to decision-making, investment intelligence is very complex and relies on a range of metrics that must be continually tracked and analyzed to precision. ROI and price-to-earnings ratio are just a few used by professional investment intelligence analysts.
What’s in It for Small Businesses?
Startups and small businesses usually have a lot to learn in terms of both business theory and practical experience. Investment intelligence is a valuable source of business savvy, as it shows the importance of strategic and continual data analysis – for growth and beyond.
To Mitigate Risk, You Need Data-Based Decisions
In today’s competitive landscape, the primary objective of every new business is to stay afloat. It’s not only the oversaturated markets that aspiring entrepreneurs need to worry about but also the uncertain economic and political climate everywhere in the world. When times are uncertain, data-based decisions are the only way to avoid risk.
By repeating this phrase enough times – data-based decisions – business experts and consultants are hoping to underline the prevailing importance of playing with the cards you’ve been dealt. The information about the future is already out there.
Collecting as much as you can is an example of intelligent decision-making.
Alternative Data Sources Expand Your Insight
Where, exactly, should you start scraping data?
Online-based startups would want to rely on their social media listening tools for the time being, at least until their budget allows them to approach data from a more serious standpoint. Every mature business is turning towards alternative sources for external data. Like investment intelligence, this data comes from large datasets collected by third parties. There are companies specializing in data scraping that clean and organize raw datasets. They collect data from public data sources across sectors and industries.
For example, most companies find firmographic data useful for competitor monitoring.
Other data types, like demographic, contextual, and behavioral, can inform your business decisions more directly. For instance, they can help you choose the right marketing approach and, more specifically, tell you what your audience responds to these days.
The Use of AI is a Needed Boost for Data Analysis
Another lesson from investment intelligence shines some light on AI.
Whenever we’re speaking of data-driven decision-making, we must not forget that this coin has two sides. One is data scraping, which implies the use of large datasets from alternative sources, while the other is organized in-house analysis of this data.
AI can help businesses enhance both.
In fact, third-party data collectors already rely on artificial intelligence to automate data scraping. However, AI is just as important on the premises, where various AI-based tools can save both time and money while translating this data into valuable insight with great precision.
Precision and Commitment are Key to Success
Speaking of precision, investment intelligence also teaches the real value of employing a hands-on approach. Meticulous metrics, systematic processes, and continual tracking must become a daily task if companies are looking to make the most of their data.
Take an intelligence investment tool called shift-share analysis formula, for instance.
In terms of growth and investment metrics, shift-share is very specific – it measures the employment growth and competitiveness of a certain industry in a certain region. For practical or other reasons, every business should know more about shift-share and similar tools.
There is no such thing as irrelevant data for businesses that rely on data for success.
Data Has the Power to Disrupt Entire Industries
Data analysis is not only a tool for mitigating risk. The right information has enough power to help companies reveal demand gaps and obtain a competitive edge. When two or more businesses are selling more of the same products or services, data becomes a differentiating point.
Commitment to data analysis helps predict the future and remove uncertainty. If used right, this is an industry-disrupting power.
By giving you in-detail insight into current demand, customer behavior, and industry-wide competitor agenda, data allows you to predict how these variables will change in the future. It doesn’t get any better than this when it comes to decision-making and investing.
Conclusion
Investment intelligence depends on shift-shape analysis formula and similar tools to best use the acquired data. A valuable lesson that every business can take from this is – the more in-depth the analysis, the better. Under constant examination, data is a transformative force.
Also read: Effective Benefits of Using Sales Commission Software for Branding Purposes
