May 19, 2020

Big Data and analytics: mining information for value

Will Girling
5 min
 Big Data and analytics: mining information for value

Big Data and analytics have become omnipresent buzzwords recently, but what do they mean for how businesses should operate? Business Chief explores the subject with Abel Smith at Tech Data

It’s often said that, in our modern economy, data is becoming the new oil. Whether this metaphor is totally accurate is almost beside the point; in an increasingly digital world, everything is data, a fact that becomes ever more pertinent when the tools available for collecting and analysing information evolve. The scale of data’s explosion was estimated by Domo to reach 1.7MB of new information every second for every person on Earth by 2020, with an approximate total of 40 zettabytes (40 trillion gigabytes) globally. Contributing to this enormous volume is ‘Big Data’ - large quantities of information pertaining to corporate assets, which require highly innovative forms of processing to decipher and render useful for decision-making within business.

Abel Smith, Director of IoT Solutions at Tech Data, believes that how a company chooses to analyse its data can have a significant impact on enabling efficiencies. After all, when it comes to Internet of Things (IoT) devices, the value a customer derives will not necessarily be from the device itself, but rather the wealth of insights and options for action that the analysis of data can make possible. “Businesses, small and large, need to aggregate, unlock and organise their data so it is accessible and can be maintained whilst being secure and ethical. When that is in place, analytics can be used to visualise, gain insights and drive even more value with artificial intelligence (AI) and machine learning,” he says. 

AI-powered analytics

The premise of AI-powered analysis is rooted in the goal of designing technology that can perform tasks normally reserved for people. According to SAS, machine learning forms an independent subset of AI and focuses on training a machine to identify patterns in data and then ‘draw conclusions’ from it in a similar way to the human brain. First, machines are given ‘inputs’ and their associated ‘outputs’ in order to generate a prediction algorithm. Next, they are presented with a new input and use the set algorithm to predict an output - the ultimate goal being to refine the algorithm until the error margin between the machines’ prediction (called the ‘cost function’) and the actual output is as close to zero as possible. Therefore, machine-learning-based analytics represents a cycle: data is collected, an algorithm is formed and used to make a prediction, the result is collected and analysed, repeat ad nauseam. 

By investing in these next-gen forms of analytics, vast amounts of data, which would otherwise be wasted, can be transformed into a highly valuable asset. “By analysing the usage, the channel can begin to take a number of actions. For example, the data can give resellers and systems integrators an understanding of what challenges their customers are encountering and what additional services they might need in order to solve them,” says Smith. The seemingly infinite streams of data generated on a daily basis take on a whole new dimension, as each piece can be used to better inform executives on how to steer corporate strategy. “Information and dialogue can result in continual improvements, adding value for the end customer and helping to create lasting relationships built on meeting real-world business objectives. It also helps with securing and onboarding new clients, as the process of continual development highlights and helps you open up new markets.”

“For those companies that can bridge the gap between IT and business objectives, there are major opportunities for success,” Smith adds. But what does this mean for Big Data and analytics? 

Extracting the value of data

For many companies, this will mean finding ways to improve the end-user experience, with data analysis providing the engine to solve larger volumes of problems than ever before. In an article by McKinsey & Co, Victor Nilson, SVP at AT&T, explained that the company uses data analytics to optimise customer care. “We’ve used Big Data techniques to analyse all the different permutations to augment that experience to more quickly resolve or enhance a particular situation. We take the complexity out and turn it into something simple and actionable.” Other companies might leverage data analytics to improve the operation of a product itself, although some, like Vince Campisi, Chief Digital Officer at United Technologies, consider both forms of optimisation to be intrinsically linked. Campisi told McKinsey, “We’re starting to enable digital industries, like a digital wind farm, where you can leverage analytics to help the machines optimise themselves. It’s an example of using analytics to help a customer generate more yield and more productivity out of their existing capital investment.”



The opportunities afforded by Big Data are practical and abundant for companies dedicated to developing innovative ways of analysing the available information. Smith remarks that, although the modern era is one of “digital supremacy” and technology is undoubtedly indispensable to nearly every industry, there is some hesitance - even fatigue - among executives for digital transformation schemes that under-deliver. However, the eminently practical and widespread advantages of streamlining via data analytics is an opportunity that should be fully embraced. “If there is one thing that businesses are interested in, it is how they can be more efficient, open up new growth, or be more compliant,” he says. “For those in the channel that want to continue to succeed, the focus has to switch from technology to business outcomes.”

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Jun 12, 2021

How changing your company's software code can prevent bias

Lisa Roberts, Senior Director ...
3 min
Removing biased terminology from software can help organisations create a more inclusive culture, argues Lisa Roberts, Senior Director of HR at Deltek

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. 


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