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Businesses are buying all kinds of data and data analytics solutions but they may still fail to meet their business goals. Increasingly, vendors and professional services firms are offering Insights as a Service (IaaS), which can be part of the cloud stack or a new, pay-for-answers economic model. Let me explain why and when enterprises should consider IaaS.
Data represents a new class of economic asset for any organization. But enterpise data management strategy is a complex approach involving many layers. And while intelligence within that business becomes more complex, the needs for insight evolve as well. The problem is that data arrives from multiple and different sources, and is both structured and unstructured (internal database and content repositories, emails, spreadsheets and crucial information coming from the outside such as webpages, blogs, social media posts etc.)
From insurances and financial institutions to oil & gas companies, from pharmaceuticals to manufacturing enterprises, telecommunications etc… each and every industry shares the same mission-critical objectives: reducing cost of combining and valorizing the information they have; mitigating operational risks and complying with regulations and policies; increasing revenues through better decisions; improving their offering and customer care services; and more. Businesses are looking for solutions to not only locate information. They need technologies and analytics strategies to help them integrate and make sense of these massive amounts of data in order to have all the insight they need when they need it.
One problem, different approaches, a unique goal: paving the way to streamlined data
It is a common experience that when it comes to searching for information, we ask around our organization, and hope to find something by running some searches through the company databases. Is that really the best way to locate necessary data? and Are we sure that this is the best journey from data to insight?
When the discussion progresses from “if” to “how” analytics activities should be implemented, not surprisingly there are many different approaches.
Given the value of such activities, bringing analytics in house and so investing on people and internal processes could be much better and more effective for the business. Whether intended for intelligence and strategic processes or operational activities, the key is combining the right tool with certain skills and businesses should be willing to invest in improving internal skills to take the maximus advantage of their investments.
But if the organization is unable to optimally manage analytics in-house, IaaS may be a good answer. As the cloud is helping businesses solve the problem, tackling the complex data integration avoiding businesses implementing several different systems, writing code or using different interfaces, as IaaS tools are an important extension of analytics applications. They support businesses to perform analytics and achieve their goals, by orienteering the data management to action.
Regardless of the analytics approach, in-house or outsourcing, the weak point may be the plethora of unstructured information that may be relevant to enterprises business decision. Unstructured data including business documents, emails, consumer comments, independent reviews, online market reports etc. have an equally important role in the ever-changing strategy to meet an enterprise’s business goals.
The value of data is established through its effective analysis
Sales performance and volumes, statistical data, growth percentages and other enterprise internal data provide a picture of where a company stands—but it is only through the capability to understand “why” that businesses can identify early on what they need. Unlike a normal search which leverages a simple keyword search, there are technologies based on artificial intelligence algorithms that uncover the true meaning that words express, in their proper contexts (semantic technology.)
Semantics understands the word and its context, regardless of the number (singular or plural), gender (masculine or feminine), verb tense (past, present or future) or mode (indicative or imperative). When analyzing data, semantic cognitive tools can decipher also emotions in context, providing an early indicator or trends, news and working signs to look out for. They help organizations analyze and make predictions more accurately because they deliver the most appropriate data.
There is real business value in incorporating cognitive computing into an overall insight strategy: by extracting knowledge from data, businesses receive information from structured and unstructured modalities—a key element to productivity, efficiency and competition.