The goal of data collection is to improve business performance, plain and simple. We collect data on customers so we can understand their behavior and partner with them more effectively. We collect data on employee performance so we can create more efficient processes. We collect data from marketing activity so we can reach more of the right people with our messages.
Data equals knowledge, and knowledge forms the basis for growth. But knowledge by itself does not automatically equal growth. If we draw the wrong conclusion from the knowledge we have, we will not see positive change. We may even see the opposite result from what we intended. And in business, that faulty conclusion can be measured in missed revenue and lost profit.
The question, then, is why our data leads us to wrong conclusions. Why do we make faulty predictions that don’t deliver the desired results? There may be many reasons, but among the most common are:
* Incomplete or flawed data
* Faulty or inaccurate analysis
* Unexpected events that change the equation
In this post, we’re going to take a look at three ways you can address the challenges that flow out of those first two problems. (We’ll leave the third one for your business continuity plan.)
Strategy #1: Improved Data Structure
Proper data structures make it possible to store and manage large amounts of data efficiently, and to retrieve that data when needed.
Unfortunately, some experts predict that 90% of the data created in the next ten years will be unstructured data. That’s because humans—unlike machines—communicate in words, not numbers. And few things are worse for data structure that a bunch of words in an open text field. But humans all too often store information in text that can’t be read by computers, while also creating data that often contains errors, omissions, or duplicates.
Poor data structure fails to create standardized methods for data collection and storage, which means our tools can’t process the data effectively.
One sample consequence of bad data structures: your data stack may fail to take into account all of the sources that need to be integrated or how that integration will look. This creates a mish-mash of unstructured or semi-structured data that makes it difficult to access and analyze the necessary information. We may store data in numerous unconnected silos that can’t communicate with each other.
The solution is to lay the foundation for strong data structure by answering questions like:
* Where will the data come from?
* How will we store the data?
* How will we standardize the data?
* How will we integrate the data?
* Who will need to access the data?
* How will our data strategies change over time?
The goal with each of these questions is to identify all the pieces of information you need to build a robust data structure before you try to analyze anything.
Strategy #2: Machine Learning
There’s a lot of buzz about machine learning, but there are a few things you should know before you purchase a new tool:
First, let me reiterate: put a robust data structure in place before you think about using a machine learning tool.
Second, ask yourself whether machine learning is the best way to address the problem at hand. Can other, less complex solutions deliver the same results?
Third, evaluate your data. Is it clean, accessible, and accurately labeled? If not, the machine won’t be able to use it effectively.
Fourth, determine the margin of error. Machine learning is, after all, learning. It constantly adapts based on new stimuli, and that means there will be a percentage of error. Speech-to-text software is a great example of this. Because accents and pronunciations differ, the software makes mistakes until it learns your individual speech patterns.
With those caveats out of the way, let’s talk about how machine learning can improve your business predictions.
Machine learning applications access data and use it to improve operations and processes—“learning” without being explicitly programmed. Applications span the spectrum from improving equipment efficiency to recommending products to customers. To achieve the greatest ROI from a machine-learning tool, you must follow a few rules. Here are some excellent tips from Tech Emergence on how to apply machine learning to business problems:
Have a specific goal in mind
Don’t make the mistake of investing in machine learning first and then looking for ways to use it. Instead, start with a business critical issue you want the application to solve and consider how you can use data to drive a positive result.
Give the data context
Data without context can lead to faulty conclusions. For example, if you’re tracking buyer behavior with the goal of creating product recommendations, don’t forget to account for activity based on sales. Otherwise, you may end up over-recommending a product that saw a lot of purchases based on a great sale, but otherwise experiences mediocre performance.
Don’t expect machine learning to operate in a vacuum
The success of any application—but especially an analytics tool—depends on the skill and knowledge of the people using it. Work closely with stakeholders and team members to effectively analyze the data and use it to drive the ROI you want.
Test and then test again
It takes time to set up an effective machine-learning infrastructure. Expect to work through several iterations to find the most useful data and identify any problems with the algorithm.
Strategy #3: Artificial Intelligence
Artificial intelligence is broader than machine learning, but it carries the same caveats. Start with a critical business problem; be sure you have clean, accurate data; and assemble a team that knows how to assess results effectively for the greatest ROI. With that said, AI holds the potential to transform massive data sets into actionable insights. Let’s look at a few potential applications:
Business dashboards pull data from multiple sources to spot trends and generate reports. AI adds additional layers to your business dashboard such as offering recommendations pulled from key data, issuing alerts based on data anomalies, or identifying new patterns in user behavior.
Sentiment analysis AI tracks patterns in text-based feedback to identify emotion. It’s an excellent tool for making recommendations based on text inputs that would otherwise have to be analyzed manually. Ultimate Software has an interesting application for HR that uses employee feedback from performance reviews to identify which individuals may be considering a job change and recommend specific manager actions to support those employees more effectively.
The manufacturing industry has seen several AI innovations that can be used to monitor physical equipment and process historic data. The Internet of Things allows manufacturers to digitize physical pieces of equipment and create AI functions to assess things like equipment maintenance needs, resource allocation, delivery schedules, and process efficiency.
These are just a few of the tools available to you as you gather business insights and make predictions based on data. Predictive analytics is a deep and varied discipline that requires several things for effective implementation:
* Clear, specific goals
* Good, clean, complete data
* Effective tools
* People with the knowledge and skills to assess results accurately
As you work toward better business predictions, build your strategy on these four pillars. Then, learn how to separate the business critical information from what is merely interesting.
You can find all kinds of intriguing data when you start looking for it. The key is knowing how to use the data to achieve business goals or enhance service for customers.
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