No data, No AI. Crap data, Crap AI 👀…
It cannot be simpler than this!
- Data is an organisation’s most valuable asset!
The effective use of data can allow businesses to separate themselves from the pack, gaining first-mover advantage to become leaders and disruptors in various ecosystems.
Data at your fingertips can enable business acceleration across all areas of the organisation. From improving business processes to help make informed decisions, to providing market insights. The goal of data-based decision-making, as with most strategic business goals, is to achieve a competitive advantage and generate value for the organisation.
With clean, quality, up-to-date data, that can be analysed, businesses can:
- gain insights into existing and potential customers (hunting smarter)
- meaningfully engage with customers, through personalised interactions (with consent and respecting privacy of course)
- make business decisions with confidence (provided they trust their data)
- add value to the organisation (Data = IP)
- better inform product and service development to address market needs and opportunities
2. Data Quality is paramount
With dreams of AI fixing all your problems, it can only be as good as the data you feed it.
🤔 Can you trust where the data came from?
🤔 Should AI trust where the data came from?
When things go wrong, Don’t blame Technology, don’t blame AI, blame the humans that designed it in the first place 👀
AI will get just as lost in your data as your human colleagues… same rules for data cleanliness still apply..
- Data Accuracy — Data should be precise it should contain accurate information. Precision saves time and money. Is the information correct in every detail?
- Data Completeness — In today’s world of dynamic data any relevant information may not be complete at all times, however, at the time of its usage, the data has to be comprehensive and complete in its current form. How comprehensive is the information?
- Data Reliability — Data should be consistent and reliable. False data is worse than incomplete data or no data at all. Does the information contradict other trusted resources?
- Data Relevance — Data should be relevant and according to the requirements of the user. Therefore, the legitimacy of the data should be checked before considering it for usage. Relevance of data is necessary in order for it to be of good quality and useful. Do you really need this information?
- Data Timeliness — Timeliness of information is an important data quality characteristic as information not timely can lead to people making the wrong decisions. Out-of-date information costs companies time and money. How up-to-date is the information? Can it be used for real-time reporting?
3. ”In God we trust. All others must bring data.” (W. Edwards Deming)
This statement refers mainly to the importance of data measurement and analysis when doing business, it can help eliminate finger-pointing by getting down to the facts.
Data trust means having confidence that your organization’s data is healthy and ready to act on. Trust is the key to making successful use of your data.
From guiding the world through its health and economic challenges to sinking millions of dollars into strategy and investment decisions, do you trust your organisations’ data?
4. Keeping it all together … Data Governance
There’s a lot of it, so some order to the chaos doesn’t go astray.
An overall data governance strategy and security is paramount to keeping it all together, to reduce risk of it all falling apart into oblivion.
5. Data assurance and an audit can give you some peace of mind that the decisions you are making are founded on reliable data.
🤔 Start with asking…. Is data secure?
How is the data secured? At the source, in transmission, in storage, in backups and it’s destruction process.
About me: I have 20 years of experience with a privacy & cybersecurity focus in Information, Communications, and Technology providing advisory and assurance to enterprises and government. Feel free to connect with me on LinkedIn or drop me a message.