The Magic Triangle of Data: Machine Learning, Automation and the Human Force Driving Data Science
It is no secret that technology is constantly changing. Although industries face an escalating tide of technological disruption, they all too often focus on disruption challenges. Instead, they need to embrace the opportunities that disruption brings.
Data has become a real game-changer. According to IBM, 90 percent of all data has been created inside the last two years. The ways we share, analyse and absorb information through technology have now exploded to the point where Big Data’s usage is far more commonplace. For many businesses, it has become the crux of their model.
Against this backdrop of constant change, a power trio of technologies has emerged: machine learning, automation and self-service human-lead data science. All three have their benefits and their place and understanding how each works individually will help construct a broader picture of how abundant data can help industries as well as enable companies to benefit from the big data boom.
Machine learning is, in essence, exactly what it says on the tin. It is a system that is taught to learn from experience. It consists of three primary components:
- Model: The system that makes predictions or identifications
- Parameters: The signals or factors used by the model to form decisions
- Learner: The system that adjusts the parameters — and in turn the model — by looking at differences in predictions versus actual outcome
With these three mechanisms working together, machine learning systems are able to output reliable, valuable, and consistent analysis.
It has become incredibly valuable because organisations are dealing with vast and rapidly growing volumes of data. Some estimates suggest that the amount of data globally doubles every two years. Attempting to find insights and value from data can be very difficult. Therefore, one of the main appeals of machine learning is that the complex algorithms do the hard work for you.
Automation within the context of data is defined by the need for machine learning models to have repeatability, reproducibility and refreshed models to provide predictions. This automation starts with fixing pain points for workers that are spending large proportions of their time in Excel and many hours each week simply rebuilding spreadsheets once new data arrives. In these common situations, automation allows for a lengthy manual task to be condensed and automated into a few seconds. This, in turn, enables anyone handling large amounts of data to spend more time running analyses and focusing on critical business outcomes.
Human-Driven Data Science
Automation and machine learning, however, have one primary limitation: context. While automated analysis is extremely effective, it is hollow without knowing how and where to apply these learnings most efficiently, as well as understanding what may have caused the trends found. This is where people capable of both analytics and critical thought come into their own. The people who wield this power come in two distinct forms: firstly, data scientists, who are simply defined as those who work in the field of data science. Interestingly, there is an emerging group of people who have become known as ‘citizen data scientists’, i.e. those who spend time crunching data in a non-data focused role. These people have been empowered to partake in data analytics by making use of accessible (generally ‘code free’) self-service analytics platforms.
Traditionally data scientists have worked in self-sufficient teams, but this is beginning to change as data has a larger and more central role throughout the business. Many of the tasks required are long and repetitive. The emergence of self-service, code-free data platforms have elevated people’s abilities at all levels of analytical awareness. The ever-increasing number of people working in data science has meant that business and lead data insights are becoming more common within short and long-term strategy that ML cannot entirely conduct.
The Magic Triangle
When all three fields of data analytics are combined, there can be an unrivalled added-value for business. The problem of using only one of these big three tools is that you will be limited in your progress. Citizen and specialist data scientists are unable to perform thousands of rules per second, while automated systems struggle with joining the dots.
Getting ahead of the competition has always been a primary focus in business but now that big data is a major player, companies are drawing on elements of this ‘magic triangle’ and beginning to adapt in a very rapid evolution; gaining valuable insight and uncovering deeper understandings of customers, markets, and the wider world.
How changing your company's software code can prevent bias
Two-third of tech professionals believe organizations aren’t doing enough to address racial inequality. After all, many companies will just hire a DEI consultant, have a few training sessions and call it a day.
Wanting to take a unique yet impactful approach to DEI, Deltek, the leading global provider of software and solutions for project-based businesses, took a look at and removed all exclusive terminology in their software code. By removing terms such as ‘master’ and ‘blacklist’ from company coding, Deltek is working to ensure that diversity and inclusion are woven into every aspect of their organization.
Business Chief North America talks to Lisa Roberts, Senior Director of HR and Leader of Diversity & Inclusion at Deltek to find out more.
Why should businesses today care about removing company bias within their software code?
We know that words can have a profound impact on people and leave a lasting impression. Many of the words that have been used in a technology environment were created many years ago, and today those words can be harmful to our customers and employees. Businesses should use words that will leave a positive impact and help create a more inclusive culture in their organization
What impact can exclusive terms have on employees?
Exclusive terms can have a significant impact on employees. It starts with the words we use in our job postings to describe the responsibilities in the position and of course, we also see this in our software code and other areas of the business. Exclusive terminology can be hurtful, and even make employees feel unwelcome. That can impact a person’s desire to join the team, stay at a company, or ultimately decide to leave. All of these critical actions impact the bottom line to the organization.
Please explain how Deltek has removed bias terminology from its software code
Deltek’s engineering team has removed biased terminology from our products, as well as from our documentation. The terms we focused on first that were easy to identify include blacklist, whitelist, and master/slave relationships in data architecture. We have also made some progress in removing gendered language, such as changing he and she to they in some documentation, as well as heteronormative language. We see this most commonly in pick lists that ask to identify someone as your husband or wife. The work is not done, but we are proud of how far we’ve come with this exercise!
What steps is Deltek taking to ensure biased terminology doesn’t end up in its code in the future?
What we are doing at Deltek, and what other organizations can do, is to put accountability on employees to recognize when this is happening – if you see something, say something! We also listen to feedback our customers give us and have heard their feedback on this topic. Those are both very reactive things of course, but we are also proactive. We have created guidance that identifies words that are more inclusive and also just good practice for communicating in a way that includes and respects others.
What advice would you give to other HR leaders who are looking to enhance DEI efforts within company technology?
My simple advice is to start with what makes sense to your organization and culture. Doing nothing is worse than doing something. And one of the best places to start is by acknowledging this is not just an HR initiative. Every employee owns the success of D&I efforts, and employees want to help the organization be better. For example, removing bias terminology was an action initiated by our Engineering and Product Strategy teams at Deltek, not HR. You can solicit the voices of employees by asking for feedback in engagement surveys, focus groups, and town halls. We hear great recommendations from employees and take those opportunities to improve.