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Leadership teams have always known the importance of data, but developing ‘high data integrity’ is now the precursor for success in all digital transformation programs. This is because it gets leadership teams closer to enjoying optimal outcomes from their digital transformation programs, which increasingly feature intelligent automation and conversational AI technologies, both of which rely on huge pools of data to drive their functionality.
The purpose of technology should always be to improve or enrich people’s lives in meaningful ways, and because data is the lifeblood of technology, ensuring its integrity ensures technology can fulfil this purpose.
So, whether you’re looking to enrich human life by delivering early warning of a health condition through a wearable, for example, or if you’re looking to enable your accounts team to complete end of month activities in a fraction of the time through process automation, your success is going to rely on the integrity of the data you apply to the solution or process.
What steps do we need to take to ensure we have ‘high data integrity’?
Because this is such a complex topic, with many different sources of information out there on how to approach it, we’ve developed an 8-step approach you can take to ensure your data can deliver on the ‘promise’ of the product, service, or solution it is driving.
Step 1: Ensure your data is compliant
Because there have been so many cases of the misuse of data in recent years, there’s lots of attention placed not only on its security, accuracy, consistency, reliability, and representation, but also its ethical use and application. Jurisdictions around the world are updating their legislation around these factors, meaning compliance requirements in the IT industry are constantly evolving to stay up to date.
Step 2: Can you manage the complexity of regulation and compliance?
Many mid-market organisations operate across multiple jurisdictions or states within Australia, and each region may have its own specific IT compliance requirements. Understanding and adhering to different regional regulations can be time-consuming and may require significant expertise. This is resulting in many mid-market organisations struggling to keep up with these frequent changes, especially if they lack dedicated compliance teams or resources.
Unfortunately, failure to adhere to the latest regulations can lead to penalties, legal issues, and reputational damage.
The Privacy Legislation Amendment (Enforcement and Other Measures) Bill 2022 increased fines for companies that don’t protect themselves against serious or repeated breaches. Penalties are rising from $2.22 million to $50 million, 30% of the company’s turnover in the relevant period, or three times the value of any benefit obtained through the misuse of the information – whichever is greater.
Therefore, deciding if you have the internal expertise to manage this complex regulation and compliance is important.
Step 3: Ensuring the integrity (accuracy) of data so it can be trusted
High-quality, accurate data forms the foundation for all intelligent automation and human augmentation solutions. Before you get into the development stage of any digital transformation program, you’ll need to ensure your data is accurate, complete, consistent, and relevant to your analysis or decision-making process. This includes verifying the sources of data, addressing any data entry errors, removing duplicate or irrelevant data, and performing data cleansing and validation processes.
Step 4: Data Integration and Interoperability
With the efficacy and integrity of your data sources assured, you’ll also need to ensure it can flow freely through your operations.
Data traditionally sits in disparate silos or locations, often with little or no interoperability. To ensure successful digital transformation outcomes, this data needs to be connected through the integration of your cloud and on-premise systems and applications, so you can exchange data and implement business workflows.
Without the right systems, processes, and infrastructure in place to gather, structure, review, analyse, refine, store, back-up, and protect your data, it won’t be able to deliver the outcomes you need.
Step 5: Security
Your Cyber Security policy needs to be capable of protecting your data as it seamlessly flows through your operations. Because your success in intelligent automation (and people enabling tech generally) is going to rely on the interoperability of cloud and on-premise systems and applications, you need to have complete control and confidence in your security systems, procedures, and protocols.
Step 6: Advanced Analytics and AI
Data has infinite potential if it can be captured, analysed, and interpreted in meaningful ways. At Tecala, for example, we combine artificial intelligence and automation into a complementary service because together they can reveal patterns and trends that people will struggle to identify easily. These are the insights organisations use to improve products, services, and decision-making.
Step 7: Data Literacy and Skills
Data is only going to deliver value if your people have the skills and training to interpret and use data effectively. Data and AI literacy should not be limited to a Centre of Excellence (COE) – they should be embedded into the organisational culture. With structured training and relevant resources, your teams will be able to understand and analyse your data, so they can make smarter decisions that deliver genuine value to your organisation and society generally. This is how you create a truly a data driven organisation, at all levels.
Step 8: Environmental, Social and Governance (ESG)
In this context, ESG relates specifically to the ethical and responsible use of data that complies with all your regulation requirements. This puts a layer of certainty and confidence into your operations that you’ll create products and technologies that align with societal values, promote trust, and prioritise the well-being of users and stakeholders.
Working in a holistic way, your ESG framework should foster a more responsible and sustainable technological landscape.
Data Landscape and Maturity Assessment – the first step in ensuring effective use of data.
A Data Landscape and Maturity Assessment (DLMA) is a review of your organisation’s effectiveness in using data. It’s the best place to start in your journey towards ensuring you have high data integrity.
The DLMA assesses the current state of your data, collaboratively formulates the future state based on your goals, and provides a gap analysis between where you are now and where you need to go.
An output of this assessment is a roadmap of projects with cost and duration estimates required to transform and elevate your organisation. It shows where your data can be used to deliver the outcomes you need, while complying to your own governance standards and relevant regulatory requirements.
Unlike other maturity assessments, which can be purely technology focused, Tecala’s DLMA provides a 360-degree view on the intended data use within your organisation, which takes into consideration your people, processes, data landscape, organisational vision, and mission, as well as looking at your cultural and ethical guidelines around how you should and shouldn’t use your data.
Having work that staff find meaningful to undertake is vital, especially when it comes to recruiting and retaining the best and brightest.
Why a people-first approach to technology and the experience it delivers is so important to modern dynamic businesses.
