Welcome to Talent Tech Labs Lookback. We spend a lot of time exploring the Talent Acquisition Ecosystem and Marketplace and while every TTL Trends Report focuses on a different theme, we occasionally like to look back at some of our greatest hits.
Old and new players alike are rapidly repositioning around how to collect, analyze, predict and package more data for greater and quicker insight. For evidence, look at last year’s announcements from established players LinkedIn and Monster, which now both aggregate jobs. The result: In Monster’s Q2 2015 earnings call, they reported a 12x increase in the number of jobs inventory and four million new users. That data has real value, and the market took notice, adding over a $100 million in market capitalization. These changes are even more profound in the private market, where Hays Technology Ventures seeks to form relationships with the next generation of technology companies leveraging data.
What big data opportunities are most important to #startups and investors? Find out in our past Trends Report V3!
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Data has long been an integral part of HR’s ascension toward the role of strategic business partner. Earlier in my career at Infohrm we regularly worked with enterprises at all levels of maturity to position their data, processes and even talent for greater business insight, a natural evolution from standardized workforce reporting and systems of record investments. But HR never really had a big data challenge. Perhaps trapped, silo’d or dark data, the term used by Gartner to describe underutilized operational data, but not big data. It is the challenge of leveraging data, big or otherwise —which is very real and for some, can offer a large prize—that we currently see startups and investor dollars chase with varying degrees of success. Provide a workable solution to a big data challenge and you increase the likelihood that your startup gains the attention of investors.
What big data opportunities are most important to startups and investors?
There are three enablers that tend to accompany the most interesting data opportunities for us here at Hays Technology Ventures:
These companies, regardless of the vertical, understand that APIs enable quick integration, greater partnership and ecosystem opportunities and ultimately speed to change. This adaptability is one of the greatest attributes for an early stage technology company changing or challenging a market.
Artificial intelligence allows for smarter algorithms that improve as data assets grow and change. This is perhaps the biggest change in recent years. Although data processing moved from monthly to near real-time over three to four short years, there was still a reliance on the sophistication of the end user. Alerts and searches were static, defined by the end user. In contrast, algorithms take away this human dependency by learning from the data without intervention. Importantly, the definition of data has expanded to take the form of user behavior, geolocation, operational, social and public profile data. If data is a currency, that currency seems to have limitless borders. Hays Technology Ventures isn’t the only one in the space looking at machine learning. In July, our friends at Workday Ventures recently committed to a venture arm to invest in data science and machine learning startups, further proof of the unique innovation happening at the earliest stage.
When I mention customer focus, it goes well beyond responsiveness and Net Promoter Scores. Customers must be treated like data donors. Users must have a clear understanding of how data will be stored and shared. From an investor’s perspective, we must factor in expected changes to regulations that will continue to keep data protection and privacy a fluid area for a long time to come. To illustrate, we recognized these attributes in Onfido, an early Hays Technology Ventures portfolio company. Onfido is the only intelligent background checking solution incorporating machine learning into the compliance process. The more checks Onfido performs, the more data they gather to advance their fraud detection and identity management algorithms, leading to a strong network effect. Background checking is only a subset of overall compliance so that should help illustrate how big this opportunity is within the wider enterprise space.
About the Author: Brian Pietras is the Head of Hays Technology Ventures. His group is responsible for engaging with future technology market leaders in spaces relevant to Hays PLC with the flexibility to make direct investments or partnerships. Brian’s background includes over 13 years of experience working and investing in high-growth technology companies.
Download the full Trends Report V3 on Big Data to explore more key insights from industry experts.
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