We are currently in the era of digital business models, where all workplaces become “digital” in one way or another. Gone are the days when organizations installed data warehouses and search engines to help people find relevant data. Many of these tools never delivered on the expectations – and the needs – of users and organizations. They were lacking in analytical power and in performance when faced with large and growing amounts of heterogeneous data. Another challenge was the need to combine analysis of structured and unstructured data, including natural language processing (NLP) for a wide range of languages.
People at these digital workplaces need information, not just data. While information must often be comprehensive to be valuable – like in a 360° view of a customer – it must also be relevant. People have no time to sift through tons of information to get to the insights that guide their actions. To help organizations sift through the abundance of information, data coverage must be total, and the delivery of insight must be intelligent and selective. This delivery of information must also match the expectations of today’s digital worker, who wants answers in seconds rather than hours or even minutes.
In this new generation of the digital workforce, there are certain tips that address the challenges of catering to this always connected society, including being proactive in delivering information and tackling unstructured data.
Be Proactive: Provide Relevant Information at the Right Time
Most users within digital organizations are not experts at mining mountains of data. All they need is relevant information in their work context, no matter where the original data comes from or whatever its structure may be. Access must be intuitive or “natural,” best of all via questions expressed in their native language.
That said, information should be delivered proactively. This requires high performance systems of data retrieval, analytics and information delivery. If the work processes of employees are known, they should receive relevant information at each point of their process without even having to ask for it. In a less formalized work environment, like a cutting-edge research facility, researchers should be able to “subscribe” to topics of interest to them and get “regular news alerts,” including internal reports and external publications.
Cognitive search and analytics technologies pull together all relevant information easily and rapidly – and present it clearly structured according to a user’s work environment. Machine learning algorithms detect relationships between data and analyze users’ behavior and preferences to constantly increase relevance and richness of the information delivered. At the same time, data is safeguarded from unauthorized access and provide real business value, with high ROI figures in very diverse industries and business environments.
Tackle the Spending Paradox
Many companies spend almost the entirety of their IT budget dealing with “structured data,” which is information kept in relational databases and data warehouses. Yet more than 80 percent of company data is actually “unstructured,” mostly texts (from contracts to project reports and scientific publications to e-mails), images and videos. Apart from keyword searches and some metadata treatment, there is little that most companies can do with this data.
The good news is that with cognitive search and analytics technologies, they can deal with unstructured data, and even combine the analysis of structured and unstructured data without the same level of investment – and with sometimes staggering ROI.
As usual, the bright new digital future cannot be “bought” with a new piece of technology. It requires a change of mindset and a change in corporate culture. Nevertheless, be aware that the digital workplace technology you select can either facilitate or impede adoption and change of culture.