Tech advancements for your Enterprise Data Strategy

Enterprise Data Strategy success can only be built on a bedrock of rapid, agile initiatives using data to address critical business initiatives, underpinned by strong enterprise governance and data classification. And for most organisations, this involves a substantial cultural shift.

If you are new to developing your Enterprise Data Strategy be sure to read Blog #1 and blog #2.

It is important for organisations to recognise the need to continually adjust your Enterprise Data Strategy to cater for upcoming technological advancements. Whether they be in the short, medium, or long term, they can drastically influence your approach. Tech advancements in this area are upon us and things change rapidly so it is important to keep up to date with new approaches.

These are some examples of forthcoming developments that we believe will impact the way in which enterprises provide secure, useable, and reliable data sets:

Low to No Code Machine Learning services are becoming available for all to use, potentially reducing cost overheads by allowing data science projects to be engineered by data engineering staff. This technology may be easily adopted by all organisations needing to implement data cleansing during the process of ingesting data (via Extract-Transform-Load or similar). One example could be cleansing of IoT data used in the resource sector as it is ingested. Another might be address management by health care services, helping keep patient address data as accurate as possible. The inherent quality improvements mean data engineers should be able to tackle what are currently data science projects, thereby negating the need for expensive data scientists to make sense of incomplete or lower-quality data.

Automatically Scalable Graph Computing will scale to accommodate data sets of any size, from hundreds to billions of records. This will provide cost savings as it will no longer be necessary to pre-select a platform of a given size. Enterprises will be able to use it to gain rapid insights across massive data sets - identifying expected or unexpected relationships between data elements and more quickly driving business benefits.

Artificial Intelligence (AI) is already empowering rapid technological hardware advancements from edge and personal devices to data centre and even chip design itself. In turn, many organisations are developing advanced designs that take a holistic approach across hardware and software. Apple’s M1 series of processors were developed using advanced AI techniques, producing the best performance per watt chip in the industry. Using AI in the development of industry specific hardware and software speeds up the development of bespoke solutions and maximises hardware utilisation.

Automatic Machine Learning (ML) for data processing and analysis is becoming increasingly valuable. The use of Machine Learning for data analysis and processing is not new. However, use of automatic Machine Learning processes in a data pipeline to cleanse and validate data sets of billions of records is fast approaching maturity within multiple cloud vendors. Machine Learning as a data cleansing or analysis service will become universally adopted, as opposed to the current situation in which it is a specific Platform as a Service (PaaS) offering from a few vendors. This may be used by all industries processing and analysing data to speed up data cleansing and master data management routines. A key benefit is the automation and acceleration of data preparation activities in creating all-important information assets, thereby enhancing speed and quality of decision making. 

Quantum Computing is a completely disruptive advancement that will revolutionise anything needing compute power for analysis and design. Estimates vary regarding when it will become available in the mainstream, but it will allow organisations to tackle problems that current compute power is incapable of solving. In 2019, a calculation that would normally take the world's fastest supercomputer 10,000 years to solve was processed within minutes by a quantum computer. Plus, traditional data encryption methods could be easily cracked with quantum computers, so newer encryption methods will need to be developed and adopted for complete data security scenarios.

A smart Enterprise Data Strategy is one that involves incremental learning and adaptation. Over time, this helps the organisation to embrace a data-driven culture and use its ‘new oil’ data asset to its maximum potential. It is the best bet to achieve success in the near-to-medium term and to continually position your organisation to realise the benefit of current and future technologies.

To learn more about developing a smart data strategy download our whitepaper here.