Data: how to manage your company’s most valuable commodity
Christy Haragan, Principal Sales Engineer and Global GDPR Lead at MarkLogic, explains how she thinks companies need to structure and manage their data, and the issues they might face. Data Infrastructure is a topic in this month’s Business Chief and Haragan offers comment on the subject from 15 years’ worth of experience in the IT world.
How valuable a commodity is data?
Business is a science in today’s world, and data is what drives effective decisions – so data is arguably the most important asset any company has. We all know that the best product in the world is useless unless you can sell it. Selling it means knowing which market to address, what messaging to use and how to reach out to do so. Data is the most important component to answering these questions and achieving these goals.
Are some companies getting it wrong?
Where organisations fail is when they take decisions that aren’t backed by the right data. Knowing what data you need to have is thus equally critical. The challenge we face in the modern world is data overload - we have a paradox in that data is what enables us to sell effectively and operate efficiently, but that we have so much of it that it can seem impossible to find the right data.
How is ‘big data’ being used in the market these days?
The worlds of ‘Big Data’ and ‘Data Science’ are relatively new fields. However, the more traditional field of ‘Data Management’ has become more important in this new era of data-driven decision making. People have learnt that simply ‘dumping’ data into a ‘data lake’ will result in them ending up with a ‘swamp’. In fact, statistics have shown that data scientists, those highly skilled, highly expensive PhD hires, spend 80% of their time simply trying to manage data.
So why do people go down this route of dumping data together? Because data sits in silos. A medium-sized organisation will typically have 100 or so systems, while large organisations can have thousands. And this is to say nothing of the external data feeds organisations are looking to leverage. Systems were designed to solve a particular problem with a particular set of data, not to integrate different types of data together. Organisations spend billions each year in integration software, but traditional approaches are failing to keep pace with business and the rate of change of data.
What are the key steps organisations need to take to ensure they structure and manage data correctly?
Agile data management. What does this mean? Data management typically follows the same process of building a bridge:
- Gather all requirements (the possible questions we would want to ask of the data)
- Analyse these to build a data model (something that captures all the data points necessary to answer the questions)
- Go to each data source or system that will be required to fill in the data model and look at their data models (which will have been built to serve the original purpose that data is used for)
- Merge them all together to make them look like your target model
Only once all this is done can you actually start asking questions about your data. However, by then the business will have moved on, new data sources appeared, and people have gotten so bored that they’ll have likely opted for the ‘dump the data in one place’ approach previously mentioned.
Instead, an agile data management approach involves trying to answer one question at a time – working through just the data that supports that question, and then delivering those to our data scientists and letting them drive what further questions they wish to ask to improve their analysis. With data management improved, data scientists will be able to focus more of their time on gaining appropriate insight from data, which will give businesses quicker and better insights that will help them drive decisions that will help them grow.
Data management and compliance
As a final note to make, agile data management does not equate with process-free data management. Governance, security and control are all aspects of traditional data management that help enable the business to trust that the data is fit for purpose. As the data is brought together and treated in an agile data management fashion, part of the process in doing this should be adding metadata (information about the data). This meta-data would include details like: Where did the data come from? Is this data personal data (necessary for GDPR compliance)? What quality requirements does this data have? Adding this metadata means that the data can be classified and brought under specific controls and processes to ensure it is held to the necessary standards and governance required by the business and by law.
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.