Big data has moved from a trendy business buzzword to a must-have initiative for companies of all sizes, across all industries. The data collected gives businesses unprecedented insights into their customers, competitors and markets. According to information provided by Domo and published on Mashable, online consumer activities on social media alone contributes to a mind-boggling amount of data:
“In one minute, email users send 204 million messages, Amazon makes about $83,000 in online sales and Apple users download 48,000 apps. On the social front, Facebook users share 2.46 million pieces of content [and] 277,000 tweets are tweeted…”
Those stats only take some consumer behavior into account. Imagine the global statistics when combined with the data generated from other everyday personal and business activities. The data points are staggering, but data itself is not of much use on its face. In fact, many companies struggle when it comes to making sense of their raw data, which has led to a bit of a big data backlash.
Turning big data into smart data isn’t necessarily difficult, it just requires the right approach. While every company’s road to smart data will be paved differently, these key guidelines will keep you on the right path.
Dumb Data In Action
Most data companies collect today is “dumb.” Some of the characteristics of “dumb” data include:
- Hard to Locate: Individuals must know the precise database location of a piece of data and they must go find it themselves. To cope, developers create basic keyword search functions in company databases, which are rarely comprehensive, given the vastness of the task.
- Incompatible with Other Data: How many databases does your company utilize? What platforms are they on? Can your CRM system talk to your inventory system in a seamless way? For most companies, the answer is no.
- Difficult to Understand: Do database name, table name and column names mean anything to the average user who needs information to make mission-critical decisions? In most cases, no. Clunky databases that only developers can navigate don’t benefit those who need data insights in real time.
Moving from Big (Dumb) Data to Smart Data
So what steps can you take to “smarten up” your data?
- Give Data Meaning: First you must not only describe your data, but you must also describe the relationships between data. Controlled vocabularies, taxonomies, schemas and ontologies are essential for meaning. Rich data descriptions allow users to manipulate data themselves without relying on developers and data scientists for every mission-critical action.
- Add Context to Your Data: A high-blood-pressure reading could be a fleeting response to an outside stimulus, or it could be a sign of a critical condition. It’s all about context. The same holds true for business. To make data smarter and provide that context, the entire lineage of a piece of data should be linked from source to transformations.
- Retire Tubular Structures: NoSQL environments have moved away from tubular data structures as relational data becomes more critical. Graphs are a simple illustration of data relationships, allowing users to spot hidden trends and uncover insights quickly and efficiently.
- Approach with Agility in Mind: Businesses cannot possibly know every data relationship they will need ahead of time. Remaining flexible and agile will ensure the company can get the most from smart data, when they need it.
The best way to ensure you can take big data to smart data is with the right team of professionals in place. If you are a looking to build an innovative internal tech team that will “smarten up” your data, OnBoard Recruitment Advisers can help. Our professional recruiting team has built an ever-growing network of highly skilled big data and analytics professionals, and we strive to know the industry inside and out. If you are looking for a big data recruiting partner with a proven track record of success in the big data niche, contact us today to learn more about our process and our results.