Digital Transformation

Blending ICT and knowledge management to drive innovation

Organisations consistently develop unique understandings on both micro and macro levels. Knowledge management processes are designed to turn this information into resources that aid the pursuit of objectives. Having become an established discipline in 1991, knowledge management has, like the majority of modern organisational practices, been improved by advancements within the field of information technology.

The tasks of amassing, disseminating, managing, and leveraging knowledge can all be enhanced by a digital transformation roadmap that pays due to their importance. Indeed, if an organisation does not have knowledge management frameworks in place, or even if they are poorly or loosely defined, a digital transformation strategy should address this as a matter of urgency.

Below, I will outline the infrastructural changes that are likely to be needed for the creation of a centralised and accessible knowledge base. I will also make note of strategic approaches capable of enrichening this resource, increasing the likelihood of it delivering innovative approaches and substantive improvements to performance.

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Author:

Andy Murtagh
Andy Murtagh Technical Development Director

Enhance your storage infrastructure

Storage media is a distinctly unglamorous and, owing to its pervasiveness, seemingly unremarkable form of technology. Nonetheless, it plays an indispensable role in an immeasurable number of processes. The various practises that form knowledge management frameworks are particularly reliant on this tech.

The creation, analysis and dissemination of data is the lifeblood of effective knowledge management. In 2018, a study revealed that a total of 26.8 billion gigabytes of data were being created every single day.1 A single organisation’s contribution to this figure will be minimal, of course, but it’s a startling figure that reinforces the need for infrastructure offering substantial storage capacities being required to effectively manage knowledge.

In 2016, it was discovered that the average company managed 163 terabytes of data. This figure increased to 347 terabytes for the average enterprise.2 Attributable to the considerable value that portions of data inevitably hold, these figures are certain to have grown in parallel with awareness of big data’s capabilities. And it is these capabilities that make high-storage infrastructure an essential component of knowledge management frameworks. With all data potentially capable of providing valuable insight, it must be vetted entirely before any can be expunged.

Whilst high-capacity infrastructure would historically have been expensive, its virtual equivalent is both affordable and easily accessed. Spending on these assets is also easily optimised with organisations able to purchase only the total storage they require, but scale instantaneously whenever additional resources are needed.

Renovate top-down structures

Traditional leadership models are reliant on clear hierarchies where knowledge is created by senior stakeholders and disseminated down chains of command. Such models fail to take advantage of the often-considerable information front-line employees possess by virtue of experience. An organisation’s C-suite are unlikely to engage with customers in the same way an account manager is, for example. The latter’s understandings of customers’ pain points will be different – and valuable – as a result.

Individual experiences also shape outlooks and directly influence the way we form ideas. Circumstances are often chaotic and our experiences random. Innovative thought is, as a direct result, not the preserve of an organisation’s leaders. It can originate from anyone and expression should be encouraged.

Somewhat perversely considering my previous assertion concerning large data sets, it must be noted that assumptions based on micro interactions are not devoid of value. The understandings an organisation can derive from small data consistently bridges knowledge gaps. This, though, is dependent upon the implementation of platforms that provide all employees with the means to seamlessly share knowledge. Fortunately, numerous out-of-the-box applications are available. In the event that none are suitable, a piece of bespoke software can be commissioned.

Engage employees

The existence of a platform through which employees are able to share ideas will not result in the greater dissemination of internal knowledge. This can only be achieved by creating a culture that prizes ideas and innovative thinking; a working environment that truly engages employees and nurtures their emotional connection to the organisation.

At first, it may be necessary to encourage or even cajole employees before they will contribute. The benefits of fostering such a culture, though, stems from the cyclical relationship between listening and engagement: employees who believe they are listened to are 4.6 times more likely to feel empowered and make a telling contribution.3 In short, knowledge management frameworks are reliant upon widespread employee engagement, but if implemented correctly, also drive engagement. Furthermore, studies have shown that engaged teams generate 21% more profit4 whilst disengaged employees are estimated to cost companies a total of $550 billion per annum.5

An engaged workforce will do more than contribute to a knowledge base. It will enhance your organisation in every conceivable way.

Broaden knowledge accumulation with automation

The hierarchical spiral model for knowledge management, first put forward by researchers from the University of Wollongong’s School of Economics and Information Systems, contests that new organisational knowledge originates exclusively from individuals.6 Developed in 2006, this theory is outdated in the era of advanced algorithms, big data and automation.

Valuable information is created daily and shared freely via the internet. Ongoing digitisation of systems and the continuous collection of data are further trends that can be leveraged to develop knowledge without the direct involvement of individuals.

Whilst manually extracting valuable information from internal and external digital resources is laborious, the various processes can be automated. The majority of organisations will need to enlist the assistance of a technology partner with experience in developing machine learning algorithms and associated interfaces to achieve this, but the benefits of doing so will quickly become apparent. There is a growing body of evidence illustrating how such a resource can aid processes such as eDiscovery7, programmatic advertising8 and even the creation of effective treatments for COVID-19.9  Each serves to prove how powerful bespoke algorithms are with regards to the identification and classification of valuable information. Development costs will quickly be offset by gains in employee time that would otherwise have been lost to research.

Optimise retrieval methodologies

Once the necessary infrastructural, cultural and technological changes have been made, it’s logical to begin the process of building a digital knowledge repository. It may be necessary to harness APIs in order to feed all of the information you’ve identified into this resource, particularly any applications you may have used to allow employees to submit ideas. The processes that then govern the retrieval of information, though, can easily be overlooked, thus making any such system inefficient and a source of user frustration.

The inclusion of a tagging system tied to an index is a straightforward and effective means of addressing this problem. Any information that is added to the repository can be assigned an appropriate label, sub-label, etc. and then located via a simple menu.

For greater efficacy, search functionality can be built into a repository. This will require further development time, but the resultant algorithm will be able to do more than analyse content and its creator’s expertise, it will consider the way users interact with information and learn accordingly. As employees use the system, the algorithm will continuously consider their behaviour and improve search results dynamically.

References:
  1. DOMO (2007) Data never sleeps 5.0, https://web-assets.domo.com/blog/wp-content/uploads/2017/07/17_domo_data-never-sleeps-5-01.png
  2. IDG (2016) Data & Analytics Survey, https://cdn2.hubspot.net/hubfs/1624046/IDGE_Data_Analysis_2016_final.pdf
  3. Salesforce (2018) Why Equality and Diversity Need to be SMB Priorities, https://www.salesforce.com/blog/2018/02/why-equality-and-diversity-need-to-be-priorities
  4. Gallup (2017) The Right Culture: Not Just About Employee Satisfaction, https://www.gallup.com/workplace/236366/right-culture-not-employee-satisfaction.aspx
  5. HR Dive (2017) Study: Disengaged employees can cost companies up to $550B a year, https://www.hrdive.com/news/study-disengaged-employees-can-cost-companies-up-to-550b-a-year/437606/
  6. Sun, Zhaohao and Hao, G., University of Wollongong (2006) HSM: a hierarchical spiral model for knowledge management, https://ro.uow.edu.au/commpapers/36
  7. Xerox (2016) What Are Custom Algorithms, and Why Do They Matter for Legal and Compliance Risk Management?, https://s3.amazonaws.com/documents.lexology.com/fe640cea-ffce-46c0-ab05-51b28d974485.pdf?AWSAccessKeyId=AKIAVYILUYJ754JTDY6T&Expires=1602237610&Signature=l5G1aX%2BfLacNrfqiaYqs4h3A3Dg%3D#page=1
  8. Campaign (2019) Brands should build their own algorithms in 2019https://www.campaignlive.co.uk/article/brands-build-own-algorithms-2019/1522842
  9. Journal of Computer Science & Systems Biology (2020) A Study of Using Custom-Clustering Algorithm for a New Treatment of COVID-19, https://www.hilarispublisher.com/open-access/a-study-of-using-customclustering-algorithm-for-a-new-treatment-of-covid19-47053.html

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