The DAMA Data Management Body of Knowledge estimates that organisations spend between 10-30% of revenue on resolving data quality issues.
With volumes of data growing exponentially, it is vitalfor organisations not just to
use high quality and trustworthy data to make effective decisions, but to take a step further and become data-led –making data the most important organisational asset and putting it at the centre of decision-making processes.
At a foundational level, organisations need to put in place a data strategy that unifies relevant data sources, enables advanced analytics, and harnesses Artificial Intelligence (AI), Machine Learning (ML) and deep learning algorithms. So, what are the challenges and advantages to businesses when adopting such a strategy – and how should they go about implementing it?
The rise of edge data and analytics
The rise in IOT devices and 5G wireless connections is transforming the waydata is created and consumed. Data on the edge is data from IOT and mobile devices, be it sensory, video, telemetry or audio, that is generated every second.
There are two key challenges with mastering edge data and analytics. First, the vast amount of data accumulated needs to be managed and processed into a suitable format to get useful insights.As it currently stands, the process can be inefficient, costly and create a wider range of vulnerabilities. For example, the costs of bandwidth can be staggering, with the quality suffering due to network latency issues.
Secondly, edge analytics can be inefficient if the data is analysed in order to build a model, because it requires retrieving all the data, processing it and then pushing it back to the device on the edge.
Both challenges can be addressed with the help ofedge analytics, where organisations can let the model lead the process rather than analyse the data to build the model. With edge analytics, the information processing is happening on the edge, where data is created and consumed, creating huge efficiency gains and reducing costs in the long run.
Every modern business that has the ambition of being data-led will need to consider edge data and analytics in the future.
An example of a company that is making great strides towards being data-led is Tesla, whose cars are collecting vast amounts of real-time data on the edge. All the actions and processing are happeninglocally, rather than the databeing sent to a cloud-based service and thenbeing pushed back to the car.
In fact, it’s real-time analytics on the edge that is taking ideas like autonomous cars from theory into practice. Advancements like edge data analytics are also creating some further exciting opportunities, especially when combined with cloud computing.
From data to insights in the cloud
The cloud is the single biggest enabler for data analytics innovation, but it must be leveraged in the right way – and this can be different for every business. There’s a common misunderstanding that moving to the cloud means moving all data to the cloud.Depending on individual business requirements, this may not always be needed.
Challenges around transferring data and the speed at which this happens mean that organisations must consider what is absolutely necessary to move to the cloud to avoid any errors, confusion or compromises in quality.
The critical element for businesses is ensuring that the insights gained from data are availablein the cloud.
Business decision-makers don’t typically need the raw data, but rather the interpretations derived from processed data. By storing insights in the cloud, the right reports can be accessed instantly, making well-informed business decisions based on real-time data.
Creating a ‘digital mosaic’ of cloud technologies
Organisations in sectors such as financial services and healthcare that rely heavily on data analytics, oftenrun complex, legacy IT infrastructures which are forcing themto rethink their core architecture to support real-time (edge) analytics.
A ‘digital mosaic’ of cloud technologies enables organisations to deploy cloud-based technologies in a rapid and flexible way, building a ‘Lego-like’ framework of services and applications to suit their unique needs.
This hyper-personalised approach gives organisations the freedom and resources to be flexible, cherry picking the components they need. With each component acting like a Lego brick, they have the opportunity to select only the best technologies for each product or service they want to offer.
Be it advanced data analytics, edge data or cloud-based data management, businesses can now rapidly add or replace a functional ‘tile’ within their digital mosaic without disrupting the whole stack. They can move their legacy systems to the cloud or (if they are new entrants to the market) build a digital business from the ground up.
Data-led enterprises will be owning the future by securing the competitive advantage of making effective business decisions based on real-time data in a fast, flexible and cost-efficient way.
