Data optimization is the process of ensuring you are extracting the most value from your data at every stage of its journey, from capture, storage, maintenance and security, to its analysis and insights, and finally its archiving and deletion.
But if that’s what it is, what does it entail and require? It sounds like an enormous amount of work and potential upheaval, so to help, we have broken it down into 12 of the most important steps.
12 Steps to becoming data optimized
Data Privacy Regulations
Data privacy laws are complex, widespread and evolving. From Europe’s GDPR to California’s new privacy legislation, CCPA, as well as industry-specific regulations, it’s essential to identify which frameworks you must adhere to, plus any that may become applicable as your future strategy evolves.
Is what you want to achieve with your data ethical? This is a question of more than whether it is line with the regulations (see the previous point), but is it in line with their underlying sentiment and purpose? Where does your project sit on the spectrum of using data to deliver services, insights and capabilities that are genuinely equally beneficial to them and you, versus exploiting their data and privacy (and potentially legal loopholes) to largely your ends?
Privacy by Design
Have your projects, processes and core activities been built with privacy in mind? Privacy by Design helps businesses be proactive when it comes to data privacy, ensuring it’s planned into the project from the very start, and in such a way that privacy does not hinder any ambition or objectives. If privacy is an afterthought and implemented retrospectively, then it will invariably restrict functionality and diminish the project’s effectiveness. Privacy should never be an obstacle to progress, only a safeguard to ensure that progress is ethical.
With cybersecurity attacks on the rise, businesses need to go above the standard anti-malware and ransomware technology to protect their data. This includes implementing IT security practices that combat social engineering, such as multi-factor authentication and employee awareness training.
Migrating to the cloud (whether public, private or hybrid) not only gives you workforce access to your data and a wide range of applications from anywhere – the key to making your data work harder for you – but it also provides the flexibility and scalability your growing business requires, while removing the cost of managing and maintain on-premise equipment.
includes the ongoing maintenance of your cloud environment – incorporating connectivity, capacity, resilience and cost-efficiency amongst others – but also the need to maintain vigilance over your data residency and continuous regulatory compliance.
The right productivity tools
Improve collaboration and productivity within your business by introducing the right tools, like Microsoft 365. Key to your selection of platform is whether your employees will be able to share data within their teams and externally – and do securely, reliably and compliantly – and also whether they will be able to collaborate. Remember true collaboration is more than sharing files and working on them simultaneously. It also includes efficient project management and the ability to reach answers faster by using dialogue-based tools such as IM and video conferencing instead of relying on outdated email and attachments.
Data is arguably the greatest cost for any IT function – and certainly the one most likely to grow and, if not managed appropriately, create uncertainty.Cost management includes capacity planning, SLA management, granular understanding of costs per department, service function and project (not just per technology), forecasting in line with business strategy and integrating with Risk and Supplier Management.
This, and the two points below, are the key to data optimization. Taken together, everything above is simply typical IT processes and strategy. But in a modern business, their purpose is to prepare the business for these next three points, where data’s value is actually exploited.
Data analytics is the process of using data to make informed decisions. This may be a case of simply using performance data to learn how to improve processes or projects, or maybe using historical data to predict outcomes. These processes however rely on human interaction to query data and test assumptions.
Meanwhile, Artificial Intelligence, and Machine Learning in particular, is where this human interaction is removed. Instead the responsibility for testing assumptions, learning from outcomes and evolving the algorithm(s) is handed over to the machine. All at a speed and degree of complexity that no human team could ever reach.
Your business needs to be as productive as possible, which will inherently require greater collaboration. Tools such as Microsoft Office 365 are ideal platforms for data-sharing, communication and project management – and even data insights. Make sure your business has the skills to not only deploy and maintain these tools, but also to train your teams in their effective use.
For many businesses, the greatest data optimization benefits are often gained from identifying what processes drain the most resource time, or are most repetitive or formulaic – and probably demoralise staff the most! Many find these are comparatively simpler matters of automation rather than the more adventurous deep insight projects. For example, automating the process of rejecting or progressing inbound job applications, or helpdesk support and first line customer service, or as many accountants do, automating the collection of documents from clients for personal tax processes.
Data governance and information management
The management and structuring of the vast amounts of data that businesses consume and generate every day underpins every one of the other 12 steps above. This ranges from the need to identify data’s source and legitimacy, to simply controlling the amount of data from a cost perspective or capacity planning, through to securing it, structuring it, delivering it, backing it up, and understanding its archival requirements. Not to mention appreciating the data regulations that may apply to it throughout, and documenting the appropriate policies so they can be enforced and evolved.
After all, most innovative projects – whether AI or automation or even simple data analytics – fail because the necessary data is not available, structured or even originally legally obtained.