There’s no doubt that Data Analytics is a significant differentiator for companies looking for a competitive advantage, with the ability to deliver key insights that can help boost both sales and market share. However, the amount that enterprises can gain through advanced Data Analytics depends heavily on how well they’re taking advantage of the latest advancements in their tech and systems, as well as their preparation for future development
The business case for data-driven decision making has never been more compelling. Nowadays, it’s practically impossible to find an enterprise that isn’t influenced by Data Analytics. So much so, that it’s now a cornerstone of certain industries, such as financial services and insurance.
If an organisation wants to compete in Business Intelligence and analytics in today’s market, they can’t simply rely on instinct alone. With so many new data touchpoints, companies can now implement a range of processes to better understand the success of products, services, or feedback from customers, as well as gaining a better understanding of the status of competitors.
Once a business has well-organised data at their disposal, many of their challenges can be solved by applying the appropriate analytics process.
Data Analytics uses a range of different technologies for effective solutions, including:
ML is a subset of AI that is a core part of Data Analytics that uses algorithms that learn on their own. ML capabilities can ingest & analyse data to predict outcomes, without specified programming.
Enables users to sift through enormous datasets and understand what’s relevant and what the relationships are between data points. Today’s data mining technologies allow you to carry out these tasks at exceptional speed.
Uses statistical modelling to help determine potential outcomes and future performance, based on both legacy data and the current flow of your company’s data. By being able to identify patterns in data that are likely to emerge again, businesses can determine where’s best to focus resources, thus ensuring a better plan for the future.These technologies typically use statistical algorithms and Machine Learning.
In addition to these technologies, we’ve also compiled some best practices to make sure that your organisation is getting the most out of your Data Analytics endeavours:
Managing data end-to-end
Enterprises around the world are finding challenges in managing huge pools of data that are continuously flowing in from a range of sources; however Centralised Management Platforms are a solution to this.
The main functions of these CMPs are to access, analyse and unify data across a range of sources, both internally and externally. The main aim is to take advantage of the potential that data holds to make intelligible insights for refining supply chains and manufacturing processes.
By using a CMP platform, companies have a single information source to feed from, helping them to make insightful business decisions, backed by quality data. Another benefit is the ability to use new data feeds from external factors, such as natural disasters, epidemics, and weather to forecast any disruptions in their business (or associated markets), as well as any potential supply and demand levels.
Educating business users about overall data strategy & get them on board with self-service analytics
A chain is only as strong as its weakest link. The most important thing to remember is that knowledge voids are experienced throughout all organisations. However, if business users at all levels are shown how to make sense of what they’re seeing – it ensures that the data is being correctly and confidently applied. After all, it’s often the case that key decision-makers in business aren’t often combing through data on a day-to-day basis.
All companies should have an overarching data strategy in place to explain how different departments work cooperatively, as many Data Analytics projects require a collective effort to get data into a consolidated platform.
Deploying Machine Learning capabilities & leveraging analytics in the Cloud
Machine Learning (ML) plays an important role in augmenting the Data Analytics process for organisations that handle vast quantities of data. As Machine Learning requires different architecture than analytics does, it’s important not to apply metrics that are pre-defined as this can skew data and block important insights.
Machine Learning works best when it trawls huge volumes of raw, granular data, in order to apply its capabilities most effectively.
In line with the general direction of software and systems in IT departments over the past few years, the cloud has shown the benefits of scalable and cost-effective solutions for Data Analytics as well.
Any business that’s looking to run Data Analytics in a Cloud-based environment should plan a clear strategy for migration and security policies as well as a culture of running automation that is accessible to the right teams.
New capabilities in Accelerated Analytics are continuously emerging that necessitates the creation of infrastructure and advanced analytics toolsets to implement new data and information requirements.
In addition to this, data governance platforms are also gaining importance by offering tools that manage the integrity, consistency, and availability of data.
Look at building a better data infrastructure
Good data infrastructure breeds good data democratisation. Here are a few tips to make sure you get it right:
What Are Your Objectives?
If you have a clear idea about what you want from your data, set that as a goal. Then, compare your efforts against this goal to track the quality of your infrastructure, as well as to understand possible improvement points.
You can use Data Analytics to solve real-world critical business issues.
Good data in, good data out. Collecting clean,correc, and consistent data is the cornerstone for a professional data infrastructure.
Get a Data Warehouse
The best place to store and get the most from your data is by using a Data Warehouse. These hold the complete structured data collected by an organisation, serving as producing an overarching view of operations and information.
Greater Data Security
Companies need to make sure that any customer data that’s collected is safe & secure, as greater accessibility inherently means more consideration for data security scenarios.
If your company is looking to make access to data easier, then the importance of data democratisation cannot be underestimated.
Who Uses Brytlyt’s Data Analytics?
Our GPU-Powered Business Intelligence Tools are used in a variety of Big Data applications at the forefront of the transformational analytics revolution, including Machine Learning, Deep Learning & Advanced Data Science.
This advanced level of Business Intelligence and analytics is used across a variety of industries, including telecoms, pharmaceuticals, defence, logistics, insurance, financial services and far beyond.
3 Reasons You Should Learn More About Brytlyt GPU-Accelerated Analytics…
- Brytlyt helps you interactively query, visualise, and power data science workflows over billions of records with a wide range of accelerated analytics solutions.
- Our end-to-end platform delivers decision support and business-critical insights.
- Built on PostgreSQL, we empower users to analyse more data, faster, and with ease.
Is your business at the cutting edge of the transformational analytics revolution?
We do this with GPU Acceleration
GPU-Accelerated Analytics takes advantage of the power and performance of GPUs (Graphics Processing Units) instead of relying solely on using CPUs (Central Processing Units). GPUs are very good at handling specific tasks incredibly well – making them perfect for taking on repetitive and specialised computing tasks at speed – like Data Analytics.
Our unique GPU patent-pending IP technology has been developed to be at the very forefront of the transformational analytics revolution. Our next-generation platform with ‘speed of thought’ analytics is built for ambitious businesses who want to harness their rapidly growing datasets of today and for tomorrow.
Tomorrow’s technology, today.
Brytlyt is the future-proofed approach to data processing and analytics. Instead of overhauling your existing systems, our platform can be easily integrated into your business environment. Plus, its adaptable functionality means that you can continue to develop the platform as your needs change.
Our unique GPU patent-pending IP technology has been developed to be at the forefront of the transformational analytics revolution, built for ambitious businesses who want to harness their rapidly growing datasets of today and for tomorrow.
We know data, and we know how to help businesses get the most out of huge amounts of data – in an instant.
A Truly Smart Integration
Choosing from the wide range of Data Analytics tools and platforms available on the market can be both daunting and confusing. Therefore, it’s absolutely crucial for organisations to choose the right tool that aligns with their needs and existing infrastructure.
Instead of overhauling existing systems, our platform can be easily integrated into most business environments. Brytlyt is the future-proofed approach to data processing and analytics.
If you would like to talk to one of our team to discuss our GPU accelerated solutions and take your first steps towards speed of thought analytics, you can contact us here.