“Information is not knowledge”, Albert Einstein said, “any fool can know, the point is to understand”.
The volume growth of data is expected to skyrocket as we approach 2020. Efforts to join the dots within the data landscape of a business, and understand the underlying stories, will become more scientific. Welcome to the new era of ‘data science’.
The external data environment: i.e. what is happening outside your business?
According to IDC (International Data Corporation):
· Data production will be 44 times greater in 2020 than it was in 2009.
· Individuals create 70% of all data, but businesses store 80%.
· By 2020, over 1/3 of all data will live or pass through the cloud.
According to the Dec 16 article in Forbes: ‘2017 predictions for AI (Artificial Intelligence), Big data, IoT (Internet of Things), and cybersecurity from senior tech executives’, findings included:
· Movement towards conversational interfaces (chatbots) will accelerate.
· Deep learning (voice and image recognition) will move out of the hype zone into reality.
· AI will inform, not just perform across industries.
The predicted source of competitive advantage is data. Businesses appear to know about AI, IoT, and all the other ‘buzz-words’ but there appears to be less clarity on the specifics of internal data environments. To win the data game, a player should be aware of, and comfortable with, the direction of travel externally, but be expert in what their own strengths and weaknesses are.
The internal data environment: i.e. what is happening in your businesses
To understand internal data, it’s important to start with the basics. If you know what the data challenges within your business are, then you can manage them.
Data basics: Inputs – i.e. your data sources
The quality of what you expect to get out of a system, process or anything else, depends on the quality of what you put there initially. Businesses should know or at least have a good working knowledge of:
· What data do you as a business collect, and has this diverged away from what had been agreed?
· Why and how, you collect data?
· Where and how, you store data?
Data basics: Processing data – i.e. how are you manipulating data?
Internal data can come from three main sources: files, data bases or sensors (i.e. devices that feed live streaming transactional data e.g. data from apps). Data processing challenges tend to centre around unstructured or incomplete historical data which is often held in stand-alone files, and the live streaming data which may not be reviewed or appropriately used within a business. To understand data processing, ask:
· What form does your data come in (structured v unstructured, text v SQL, other languages)?
· Is your data clean, complete and robust?
· Is your data process itself robust?
Data basics: Outputs – i.e. does your MI (management information) support factual decision-making?
Assuming we master input and processing, the usefulness of your data will relate to how timely, quality driven, user-focused and complete the reporting is. To understand data output, ask:
· Is your data output user-friendly?
· Is your data output free from bias, fact-based and informative?
Data alone is not information, but good quality evidence-based data, presented appropriately, should be informative. Information alone is not knowledge, but accurate information coupled with experience and awareness will lead to knowledge and to understanding.
As we head towards a data centric world, and with legalisation such as EU GDPR (EU General Data Protection Regulation) due to come into force in May 2018, now is the ideal time to invest in your data management. Benefits in terms of competitive advantage and protection from data threats, will come when you ensure that knowledge-enabling data becomes an integral feature in the fabric of your business.
… As Benjamin Franklin said: “An investment in knowledge always pays the best interest”.