With the evolution of technology our modern society is experiencing a data renaissance (1).
This renaissance is allowing greater connection, greater access to information and greater opportunity to analyze our world.
What is the data renaissance? In recent years data science and machine learning have advanced rapidly. This has enabled never before seen tools and access that present the opportunity for everyone (not just the experts) to engage in data collection, analysis and understanding (2).
At Scovan we are continuously looking for technological advancements to apply to our engineering designs but also to our business processes and software. Our virtual reality innovation endeavour is one of our latest efforts in making our engineering designs accessible. We have the ability to allow our employees, clients and industry partners to virtually step into our engineered facilities and truly absorb the experience of how our designs work and function in real life. This is a prime example of technology increasing accessibility and providing opportunity for analysis by people that normally would have been excluded due to either a) geographical constraints or 2) technical understanding of engineering drawings and models.
This increased access to data comes with an escalated need for responsible collection, interpretation and distribution of information. As businesses, this responsibility for principled utilization of data is becoming increasingly evident from an ethical perspective as we witness frequent data and privacy fumbles by major players such as global tech giants and government agencies (3). However, what makes less dramatic headlines is the principled utilization of data from an operational excellence perspective.
We owe it to our organizations to take a considered approach to data employment to avoid information overload, address organizational data literacy and produce meaningful insights. We must be able to receive the multitudes of quantitative and qualitative data points injected into our daily lives, aptly separate the useful from the distracting, and actively employ this data to improve organizational effectiveness.
Data Literacy Definition (from TechTarget): Ability to derive meaningful information from data including (but not limited to) knowing what data is appropriate to use for a particular purpose, thinking critically about information yield, recognizing when data is being misrepresented or used misleadingly (4).
The challenge in this new data ecosystem is to avoid common pitfalls:
1. Measuring for the sake of measuring.
2. Failing to discern the key data versus the noise.
3. Failing to frame and record our data in a way that is useful.
4. Failing to take advantage of the software capability at our fingertips.
We must consider the end goal:
1. Be selective when choosing which data to measure (save time and energy and ensure there is value in measuring, recording and tracking).
2. Carefully evaluate the future value of each data point (filter out the noise).
3. Spend time to plan and anticipate how your data will be used, who it will be used by and frame it in such a way that will be conducive to reference and analysis (ensure efforts are not in vain).
4. Research the capabilities and intended use of your software instead of just shoving data into the system (avoid truncating your potential by underutilizing or misunderstanding the resources at your fingertips).
At Scovan we are currently focusing on optimizing our project management and productivity software to ensure we are gaining the maximum benefit from the data we are storing and the information we are communicating. We have discovered that even small tweaks in data entry can have a macro impact on evaluation of equipment pricing history and project costs.
Recognize the opportunities:
1. Previously islanded information can be connected to a cause and effect within the organization.
2. Business analysis and operational insights that were previously labour intensive are now attainable.
3. Efficiencies can be achieved by eliminating non-value add tasks and optimizing or automating worthwhile ones.
Automation has proven so effective in Scovan’s drafting and document control processes that we are emphasizing replication strategies and automation in our future round of collaborative cross-departmental initiatives.
We can apply these data science concepts and opportunities to all areas of our businesses; from entire information management systems, down to individual data entry practices. At Scovan we are using the building blocks of data literacy, technical innovation, continuous improvement, organizational transparency, cross departmental collaboration in our quest toward continuous intelligence (5).
Stay tuned for Part 2: Continuous Intelligence as Scovan continues to optimize our technology and processes in order to position ourselves for excellence in this age of the data renaissance.
Sources:
(1) https://www.ibmbigdatahub.com/blog/want-compete-master-data-renaissance
(2) https://towardsdatascience.com/the-data-science-renaissance-166cddde898f
(3) https://business.financialpost.com/pmn/press-releases-pmn/business-wire-news-releases-pmn/data-breaches-compromised-4-5-billion-records-in-first-half-of-2018
(4) https://whatis.techtarget.com/definition/data-literacy
(5) https://www.forbes.com/sites/forbestechcouncil/2018/10/18/what-is-continuousintelligence/#29364cf17d25
Photo: https://medium.com/@drpolonski/the-next-decade-of-data-science-654d85dbfa70
