How to cultivate a data-driven culture with the help of machine learning and ‘smart search’

Against this backdrop, it’s critical that businesses help their employees to use data properly. However, without effective information governance, even data-savvy employees struggle to derive true value from their enterprise’s content, spending as much as 36% of their time trying to search for information, and only in half of the cases achieve success (says IDC). Therefore, a solid foundation of a data-driven culture includes rethinking the ways an organisation deals with information, as well as implementing the right technologies.

 

The root causes of poor information governance

 

Today, organisations generate unprecedented quantities of data, the major part of which is unstructured, including complex data, such as PDFs, pictures, and other documents. Using traditional manual methods for data management has become inefficient and as a result, organisations are flooded with information. A recent report by The Compliance, Governance and Oversight Counsel found that 60% of corporate data has no “business, legal or regulatory value.” Such information is commonly disorganised, often old or irrelevant, requiring high IT labour and storage costs due to its large volume.

 

Moreover, many organisations store their content in siloed systems, each having its own search functions. Predominantly keyword-based search has proven to be ineffective due to the complexity of modern enterprise content. For example, when an average employee searches for a document in a corporate storage system, he or she usually utilises fragmented information such as brand or practice names, rather than using the exact file name. Keyword-based search will reveal all possible documents that contain these words, yet the majority of which will be irrelevant to the request. As a result, the user will have to spend a significant time filtering out unnecessary information. In fact, a recent survey has found that 82% of employees believe that poor information management adversely impacts their productivity. Some organisations even classify documents manually in order to meet this challenge. However, this is time-consuming, and introduces the risk of human error – after all, tagging documents incorrectly is worse than not tagging them at all. Generating partial information in search results leads to inconsistent business practices, which might negatively impact strategic initiatives.

 

Information governance best practices

 

How can organisations help their employees derive more value from enterprise content? Ideally, employees should be able to quickly search for necessary data, as well as for information that is relevant to their request, and share it amongst colleagues and teams. This assumes that every employee can apply existing corporate knowledge to his/her task, rather than creating an asset from scratch. For that, every piece of content needs to be indexed and managed thoroughly. Such an approach allows an organisation to take maximum advantage of its content as it endorses employees’ efforts to share "best practices" and encourages connected thinking, which in turn creates new business opportunities.

 

A robust information governance strategy enables an organisation to remove the roadblock caused by mountains of disorganised data and the limitations of keyword-based data exploration, and to accelerate data literacy across the organisation. The latter leads to benefits such as improved decision-making, increased employee productivity and makes organisations more competitive.

Here are the best practices for improving information governance:

 

1. Set priorities and design the structure

Once an organisation has decided to enhance information governance, it is recommended to appoint a dedicated team. The project team should start with asking such questions as: “How should data be organised so that employees can effectively use it?”; “Should it be searchable depending on business practices or sectors?”; or “How many languages does an organisation operate with?”. To answer these questions, the team should arrange meetings with subject matter experts. This will help them to thoroughly transform this knowledge into structured taxonomies.

 

2. Enforce machine learning to overcome the limitations of a keyword-based search

Smart search should be aware of the context of the specific business environment and the user’s goals. Such context is derived from the metadata that describes what this document is about. To enable the search system to leverage metadata, it is necessary to apply to each document complex tags. While this task is virtually impossible for a human to complete due to its labour intensity and high probability of errors, automated data classification easily copes with it. Then, machine learning analyses an organisation’s content enterprise-wide, adapts to its specific environment and derives the terms and taxonomies that best describe the business context. Smart search uses these taxonomies to provide business users with accurate search results. Moreover, such search uses not only terms based on traditional single keywords, but also “compound terms” – that is, short phrases that have very specific meaning in the organisation-specific context. If the user wishes to improve their search results even more, he or she will be able to use refiners that allow them to narrow down the search results. With such an intelligent search system, the organisation can ensure employees can quickly access the information necessary to achieve their business goals as well as to define duplicate and irrelevant data to dispose of it.

 

3. Educate employees

Employees are often resistant to change, since getting used to new technologies might take them additional time. Therefore, the project team should explain the value of information governance to every employee. Moreover, senior management should take a leadership-driven approach and encourage mid-level managers to explain how the established information governance contributes to their everyday work and opens up new business opportunities for an organisation.

 

Taking such an approach to information governance, empowered by machine learning, helps organisations to build a data-driven culture as it enables them not only to organise their data, but also to change the way of thinking inside the organisation, encouraging employees to keep, share and reuse good practice knowledge. A positive outcome of robust information governance on business is the ability to leverage corporate expertise to provide better service and long-lasting value to its customers. Moreover, with such governance in place, organisations can easily identify and delete duplicate, old and irrelevant data, thus, cleaning up storage and cutting down costs.