In a previous post in this series on the rise of Enterprise Analytics, we considered the importance of data visualization advances which encourage business users to become citizen data scientists. However, an equally key component enabling enterprise citizens is the pace of information sharing. The age of search and the intelligent web conditions a user to expect instant access to data for exploration and development of hypotheses.
A thriving data ecosystem will have rapid analytic discovery at its heart, supported by rigorous science and centralization strategies. However, the manner by which data is consumed is integral to the success of this process. Business users will thrive in an Enterprise Analytics environment that makes performing self-serve analytics as smooth and reliable as possible.
Organic Growth to Data Solutions
The McKinsey Global Institute defines four stages of an organization’s data ecosystem maturity, ranging from digitizing the data to implementing advanced analytics. The second stage is making the data available via networks. Networks that unite internal data sources make data accessible for blending and incorporation into scientific inquiries.(1)
The rise of the citizen data scientist virtually guarantees that data resources previously untapped or glossed over will be explored. These resources may even generate a refreshed portfolio of core analytics. The enterprise, understanding analysts’ evolving expectations of their data, must evaluate user accessibility to both standard and contextual data sources in order to stimulate analytic ingenuity.
Unfortunately, many organizations are de facto discouraging their business users from pursuing these insights because of the constraints of IT-driven centralization strategies. As we have discussed previously, centralization is a key component of a healthy data ecosystem. However, while anticipating centralization, analysts should be given the freedom to pursue additional, contextual data sets and opportunities. Especially when equipped with intuitive visualization technologies, savvy users will crave additional data sources, and they will self-serve to get them.
Expectations and Habits will Drive Ingenuity
Google and Amazon have altered how individuals expect to be served - with speed and ease of use. If an item is not available immediately, individuals will easily find alternates or proxies. 65% percent of customers (and 69% of Millennials) say that they feel good about themselves and the company they are doing business with when they can resolve a problem without talking to customer service.(2) It’s not surprising, then, that waiting for curated data sets may sour analytics teams on the task at hand. If they can solve the need immediately, they will.
Ubiquitous connectivity enables curiosity, creativity, and informed decision-making to thrive. Comparison websites and social networks have conditioned individuals to question what they are presented and abandon previous habits in favor of a more convenient or efficient solution. Within the enterprise, analysts and business users at all levels will leverage the problem-solving expectations fostered by “Googlization” and “Amazonification”. Users will actively seek out the integration of data pools to the best of their ability, aiming to unlock major opportunities. It is the responsibility of enterprise leaders to make that integration as seamless as possible.
Look Within: Sharing is Caring
Untapped or inaccessible internal resources are sure to abound, and are the best place for organizations to start to provide easier access to more data for their analysts. Consider this anecdote from a McKinsey Global Initiative report:
“Department personnel were spending 20 percent of their time searching for information from other...departments using non-digital means (e.g. paper directories and calling people), and then obtaining that information by traveling to other locations and picking up data on physical media such as compact disks. Such wasted effort has been greatly reduced in organizations that have harnessed big data to digitize that information, make it available through networks, and deploy search tools to make relevant information easier to find”(3)
Most tech savvy users will only begrudgingly resort to non-digital means to answer their questions; 75% of tech users feel better about themselves if they can answer a question on their own.(4) This puts internal resources at risk to be ignored or neglected in favor of more accessible, if less robust, data sources.
Beyond What You See: External Data Sources
External data may be helpful in providing context for internal resources. Organizations should systematically catalogue external data sets that may provide additional context for internal sources. The volume of publicly available data continues to skyrocket, and is complemented by organizations wishing to profit from the sale of their data sets.
Governments and municipalities are making public information more available. For example, there are more than 150,000 data sources available today on data.gov, compared to only 4,000 in 2013.(5) From educational outcomes at primary and secondary schools to census data, bike-sharing usage, and neighborhood news networks, petabytes of contextual, mineable data is often free just a few clicks away.
The Number of Newly Available Government Data Sources is Increasing
Peer Collaboration in a Sharing Economy
As individuals feel entitled to expect more from their data, they will be driven to the far-reaches of the enterprise and the murky depths of Google search results to find the raw materials for immense data insights. Enterprise Analytics will thrive off the natural communities that will arise from this effort.
Business users across the enterprise be more familiar with each other’s data and better able to communicate about where and how those data sets intersect. Analysts will be prompted to engage analytics leaders in organizations and industries worldwide, gleaning technique insights and forming social networks. Sharing tips and tricks could lead to new business ventures, new analytic ideas, or data sharing partnerships. Social networks built around analytical compatriots will grow the Enterprise Analytics via peer-powered collaboration.
In our next article in this series, we’ll discuss how enterprise decision-making will evolve as peer-powered collaboration enlivens and sustains modern Enterprise Analytics.
This post is the fourth in a series exploring the five trends that Inquidia sees in the business intelligence marketplace.
The age of search and the intelligent web conditions users to expect instant access to data for exploration and development of hypotheses.
Enterprises will use storytelling, data drilling, and peer-powered collaboration to generate hypotheses and make decisions.
"Big Data: The next Frontier for Innovation, Competition, and Productivity." McKinsey Global Institute, May 2011, 112.
"Big Data: The next Frontier for Innovation, Competition, and Productivity." McKinsey Global Institute, May 2011, 97.