Machine learning is becoming ever more prevalent in business today, but we're still only at the beginning of unlocking its true potential. There is still a tremendous amount of opportunity for its application in business.
If your data is bad, your analytics and machine learning tools are useless. And bad data can spiral out of control quickly, not only throwing off predictive models, but also muddying the new data used by that model to make future decisions.
Structure and clean data is step one
To get the most out of data collected from different arms of a business, the infrastructure should be a number one priority. Think about it like this, when you lack structure and use a number of third-party sources and software cobbled together, your data will more closely resemble a pile of pick-up sticks.
When this is the case, those who rely on it to inform decisions often find themselves having to go into data management areas to hand calculate metrics. It also becomes unclear whether they are running statistics on incomplete or duplicated datasets, which skews the results.
And based on these results, your marketing team, for instance, may have great ideas on how to increase the customer base, but they may be shooting the wrong direction due to the bad data being put into their analysis.
On the other hand, if you have a streamlined infrastructure built on a system of checks and balances, it helps ensure you're gathering the right data, and that only quality data is being added to the master database. This also allows your team to easily make necessary changes anywhere along the data gathering and processing pathway to better analyze metrics and implement new ideas.
So, if you're planning to implement machine learning in your business, start by building a solid data infrastructure based on goals and priorities. It's also important to have team-wide collaboration and someone running point on data collection and clean up.
Define your data goals
Every business has some form of data coming in --whether it's through manual or automated collection. In working to solidify a sound data infrastructure, you'll want to first define your goals and objectives for the data.
Is it to identify new marketing channels or scrap those that aren't working? Is it to improve the customer or employee experience? Whatever your goals are, take a hard look at whether you currently have the right metrics in place to help you achieve those goals --do they provide a clear picture?
If you're unsure where to start, one option is to create a goal hierarchy. For example, if the ultimate goal is increased sales, the next goal just before might be increased exposure, and so on. From there, the hierarchy tree will start to branch out.
Next, identify where data points can be collected to benchmark and measure the success of different campaigns and methods used to reach your audience. Doing this will yield a list of clear-cut feasible goals that your marketing team can work with to identify the importance of different datasets.
Defining what quality data looks like and building the right infrastructure should be a team effort --your data or IT people and those on the frontlines must come together to determine what's important and what data will be needed to help inform decision making. Your marketing or sales team, for instance, may know what metrics they need to gather and IT knows what's possible and what's not.
This will also help ensure everyone understands how to leverage machine learning, reduce the number "hiccups" in workflow, and increase the general knowledge base of all involved. When everyone knows what to look for and understands the why behind the data collection, they can more easily pinpoint data issues or errors.
Assign a data point-person
Collecting, and actually putting data to work in your organization, is a big undertaking. It requires someone, or multiple people, actively monitoring and maintaining it to correct errors before they become full-blown issues, and to make sure the right data is being gathered.
This person or team should set and enforce standards for the quality of incoming data and lead ongoing efforts to find and eliminate root causes of error. Depending on the size of your company, this person might also work with the IT team on solving data integrity issues.
Putting someone in this position allows you the speed and agility needed to change or obtain new metrics as strategies pivot or new campaigns are launched. Ultimately, having a person in place to catch data quality issues will be a huge time saver for any department analyzing data output.
Make data quality a priority
Errors in data collection and processing can happen at any point in the workflow. It's imperative you prioritize building a quality infrastructure and allotting time to clean the data in an ongoing basis.
The quality levels of data must regularly be measured, sources reviewed, de-duplicated, and cleaned. Over time, as root causes of errors are eliminated, the process will become easier.
And this system of checks and balances goes for all data --whether website traffic, customer behavior data, social analytics, and the list goes on. The old phrase, "Garbage in, garbage out," applies here. Putting pristine data into the master database ensures you're making decisions based on the most accurate information available.
Planning, processing, checking, and analysis should be an iterative process, with incremental improvements made over time. Ignoring the quality of data at any point often renders that data unusable, and that results in a lot wasted effort, time and money.