According to CB Insights, the biggest chunk of last year’s $19.6 billion in total venture capitalist funding went to startups in the “business intelligence, analytics and performance management” space. There are more and more investments in data companies, companies are investing more in their data, and well, there’s just more of the stuff.
For some companies, this uptick in data volume has led to real breakthroughs.
Netflix has used many millions of hours of viewing data to better assess what people want to watch. This has led to both to stronger recommendations and to enhanced original programming from the streaming giant.
This is the promise of big data, and when an insight like this is reached, it can have a serious impact on how a company does business and its bottom line. For many companies though, the promise of big data is not being fulfilled.
Serious data challenges
A panel at the Social Shakeup called “Data is Real-Time and Ubiquitous” looked at both the opportunities and the difficulties of data usage in large companies: People like Ned Kumar of Fedex and Linda Brunner of Siemens Healthcare discussed the role of data in their organizations, and the data challenges that they face in using it to improve how their businesses function.
These sentiments are reflected in a recent report on the state of data use in enterprises, issued by IDG Connect. They found that the biggest hurdles facing companies in terms of data were poor data quality and excessive data. The most significant group of challenges was around the difficulty of extracting insights from data: for many, identifying trends proved difficult, and a lot of data was not perceived as actionable.
This makes some sense: While huge quantities of data from a range of sources can yield new understanding, reaching the point where they can make those numbers useful is a big leap for a lot of companies. Many are still in earlier stages, creating processes for collecting, organizing and storing data from across different sources. Ensuring that that data is accurate, and normalizing it so that it will be comparable across sources is a major project, even for a business without a giant, multinational footprint.
Let’s say we’re just talking about audience size and engagement data: how many people are your ads touching? How many are your social posts being seen by? How many site visitors do you have, and how long are people watching your videos for?
If your end goal for those communications is extremely specific, say, driving purchases of a single item, perfect alignment between different datasets might not be as critical for calculating ROI as just being able to track who interacted with your content and then purchased the product.
If, however, you are trying to impact a higher-level metric, like brand awareness, being able to collect, organize, understand, and compare all that data is necessary both for measuring the overall impact of your campaigns and for understanding the relative effectiveness of different channels.
Data, generally speaking, is hard: Ruslan Belkin, vp of engineering at Salesforce and DJ Patil, chief data scientist of the United States recently spoke about data products at the First Round CTO Summit. Belkin said:
Every single company I’ve worked at and talked to has the same problem without a single exception so far—poor data quality.
It’s also possible to be too focused on data to the exclusion of anything else: Tesco, the 5th largest retailer in the world, has been consistently focused on leveraging data over the last decade. They’re often cited as an example of data leadership and yet they recently reached an 11-year low in market value. According to an article in the Harvard Business Review, ”in less than a decade, the driver and determinant of Tesco’s success (its data capabilities) has devolved into an analytic albatross.”