Machine learning transforms the methods businesses use to draw insight out of their data. With the power of learning algorithms, organizations can analyze their data in new ways and build completely new data strategies.
This new technology is extremely useful for large businesses and enterprises. As a business grows, it starts to collect more and more data. At a certain point, these data sets become too big for any analysis beyond machine learning.
Machine learning can help to manage these big data sets. Businesses can perform big data analysis, leading to deeper, more accurate insight.
Machine learning can also provide businesses with novel analytical strategies. Tools like qualitative data analysis and predictive analytics can help users to unlock their data, providing them with fresh insight.
However, businesses can't just buy a machine learning tool and get to work. Machine learning is a complicated topic, and businesses that don't take the time to understand it won't succeed.
To ensure machine learning success, BI users need to figure out how they can make the best use of it. They need to know what it is, and how they can use it to drive success.
Machine Learning Basics
'Machine learning' is an approach to software design that holds that programs should be able to autonomously learn from their mistakes and make evaluative decisions based on their experiences.
Programs that can do that are called 'algorithms'. Businesses can use algorithms in any situation where an evaluation of information is important. They can automate many basic decisions that business users have to make.
To start the machine learning process, users need to train their algorithms on sets of relevant data. This way, the algorithm can understand what correct decisions look like. It can figure out the elements that go into a correct decision, and which sort of things would result in an incorrect decision.
For instance, a machine learning algorithm might be trained to pick out images that contain specific content from a larger body of content. To know what it's supposed to look for, it needs access to a data set that contains that specific content.
This goes beyond a simple database query. Regular data storage systems can easily find content in text, but machine learning algorithms can pick out patterns in images, video, and other qualitative content. Algorithms can also make more complicated decisions than a simple keyword match.
Algorithms can only make accurate decisions if the training data it used was accurate. They also need large sets of training data, so that they can properly incorporate rare situations and corner cases.
Training the algorithm is the first step towards deployment. Algorithms don't need to be trained just once, though - algorithm development is an iterative process, where the algorithm is intended to learn from its mistakes.
How Machine Learning Can Transform Analytics
It's easy for business leaders to assume that machine learning isn't all that powerful. In its simplest implementations, it doesn't seem all that impressive - maybe just a glorified keyword search tool.
However, machine learning can be an extremely powerful tool if it's given the chance. Businesses that invest an appropriate amount of resources into their machine learning strategies can see some clear benefits.
First, algorithms are better at pattern spotting and trend recognition than humans or even other analytical tools. Since an algorithm analyzes all of its data in a consistent way, and doesn't forget or miss things, it can outperform human analysts on the same data.
One place where machine learning algorithms especially excel is drawing conclusions between unrelated data sets. They're able to connect the dots in places where human analysts don't even see the dots.
Much of business operations still relies on intuition instead of data insight. Business leaders and decision makers still have to rely on their own experience to make critical choices, at all scales of an organization.
One issue with regular analytics is that they can deliver data insight, but users still need their own experience and knowledge to understand how to turn that insight into business success. These analytics can draw conclusions, but they can't use those conclusions to make decisions.
Algorithms can actually turn their insight into real decisions. By analyzing the successes and failures of previous, similar decisions, algorithms can make informed, data-backed decisions for the future.
For example, imagine a business that wants to analyze its customer reviews. It wants to know if a given review is positive or negative, so that they can figure out what issues customers are having with their product.
For large businesses, it's not cost-effective to have an employee read every review and decide whether or not it's good or bad. Businesses have all this customer data, but it's very hard for them to act on it.
Machine learning can be very powerful in a situation like this. Data experts can train an algorithm on customer data, and help it to understand what good and bad reviews look like.
Then, they can let their algorithm loose on the unanalyzed customer data. The algorithm does all the work of classifying customer reviews, and the end user can use those classifications to find insight.
Another use case for machine learning is in predictive analytics. By powering their predictive analytics with machine learning algorithms, businesses can gain better insight into how their metrics will trend in the future.
Predictive analytics is the process of using historical and current data relationships to predict future trends. A good predictive model looks at the elements that affect a data trend, models how those elements are related, and from there, makes educated guesses on how the data will trend in the future.
A machine learning algorithm can make for an extremely powerful predictive model. Using past trends, an algorithm can make effective forecasts about future trends.
Algorithm-based predictive modeling can massively boost the accuracy of a business's forecasting. If your business wants to power up their predictive insight, it'll need machine learning to help.
Forecasting isn't the only place where a business can leverage their ML-powered predictive models. They can also use these models to power what-if modeling, which lets businesses see how changes in one metric might cause changes in others.
How To Implement Machine Learning
Businesses can use machine learning for all sorts of valuable business purposes, but using machine learning is easier said than done. Machine learning is complex, can be difficult to implement, and takes care and precision to make work correctly.
For machine learning, the right BI tool can make all the difference. A good BI tool helps to make machine learning more accessible; they allow businesses with good technical support staff to access the power of machine learning.
A BI tool can't do everything, though. While self-service machine learning tools can make implementing the technology much easier, businesses still need to use best practices and follow correct guidelines.
First, businesses need to effectively leverage their big data towards machine learning. While businesses can use smaller data sets for training algorithms, the algorithm will be more accurate if it's trained on a larger data set.
This can make it difficult for smaller businesses to access the power of machine learning. To build algorithms that are actually useful, smaller businesses need to figure out how to collect larger data sets.
Businesses can't use smaller BI tools to accomplish machine learning success, either. They need fully-featured, market-leading BI tools that can handle data sets at the sizes machine learning requires. Smaller, less powerful tools just can't make it work.
Any business that wants to leverage the power of machine learning needs to make big data work for them. To do that, they need to ramp up data collection and implement a robust, market-leading BI tool.
Second, businesses need to make finding machine learning tools a priority. Businesses shouldn't try to build algorithms themselves - especially if they don't have an effective tech team or IT department.
The best strategy is to look for a BI tool that offers self-service machine learning features. These features allow businesses to configure and train machine learning algorithms, even without dedicated technical staff.
With these tools, businesses can make the machine learning process easier, if still somewhat complicated. In addition, many tools offer templates and pre-built algorithms that businesses can use on their data with minimal setup.
After the initial setup, BI users can get to work, using machine learning to power their analytics. They can use their algorithm in the same place they build dashboards and create visualizations.
BI and Machine Learning - A Perfect Combination
With the power of machine learning, businesses can completely transform their analytical strategies. Algorithms can spot trends, automate decisions, and draw insight in ways that human analysts ever could.
BI can unlock the power of machine learning, but only if a business is willing to invest in robust, market-leading tools that can handle big data analytics. To get the best machine learning results possible, businesses need to invest in the best BI tools possible.