Five Must-Haves for Real-Time Analytics Using Time Series Data

Data overload isn’t a new phenomenon.

When Thomas Jefferson was US president, he was would receive around 150 letters every 30 days. Fast forward one hundred years, Theodore Roosevelt needed a separate team of staff to handle the increased volume of letters. By the time Harry Truman was in power, these were arriving at a rate of three truckloads per day.

Today, estimates say Joe Biden receives a massive 65,000 letters per week – not to mention the emails, tweets, and social media posts going out every day – and that’s the data volume challenge every business is now facing.

But volume is not the only increase the space has seen; data has become more valuable, and consequently, the decisions based on it are more critical and growing in number. Just think of predictive healthcare, anomaly detection, predictive maintenance and operational equipment efficiency, and pre-and post-trade analytics: these are all systems built on an ever-growing mountain of data. And that’s before businesses even consider what machine learning may demonstrate in terms of trends or patterns. Which, depending on how quickly they’re found, could either provide exciting new opportunities or, on the flip side, spell disaster for teams who aren’t prepared.

But many companies, regardless of industry sector, are not making enough of these insights. They are too focused on solving technical problems around their data, leading them to miss out on uncovering valuable information with it. A key reason for this is the inability of existing databases and analytics software aren’t to handle the increased demand that comes with capturing and analyzing data in real time. With this in mind, here are five must-haves for a modern real-time analytics engine:

  1. Time series at the heart

Most data today is time series based, generated by processes and machines rather than humans. Any business looking to harness what this data can offer should ensure they have an analytics database that is optimized for time-series specific characteristics like append-only, fast, and time-stamped. A top-level system should be able to quickly correlate diverse data sets and perform in-line calculations as well as execute fast reads and provide efficient storage.

  1. Staying open in a connected world

Alongside this, the data estate of a typical modern enterprise is considerable, and growing every day. This means any analytics engine must interface with a wide variety of messaging protocols and support a range of data formats along with inter-process communication (IPC) and REST APIs for quick, easy connectivity to several sources. It should also cater for reference data, like sensor or bond IDs, that will then enable it to add context and meaning to streaming data sets, giving the ability to combine them in advanced analytics and share them as actionable insights across the business.

  1. Incorporate historical data

By combining real-time data for up-to-date insight with historical data for past context, companies can make quicker and better split-second responses to events in the moment. Plus, this can eliminate the development and maintenance overhead of replicated queries and analytics across disparate systems. The ability to rapidly process increased volumes of data using fewer computing resources is also incredibly helpful for machine learning initiatives, not to mention reducing TCO and helping businesses to hit sustainability goals.

  1. Start early and with ease

The top thing businesses need to be looking to acquire is an analytics software built with microservices that enable developers and data scientists to quickly ingest, transform and publish valuable insights on datasets without needing to develop complex access, tracking, and location mechanisms. Complications like data tiering, aging, archiving, and migration can take up valuable time and resources which could be better used to concentrate on creating insights for important wider business decisions. Natively integrating with major cloud vendors and making this available as a fully managed service should also be an important consideration if businesses are seeking to onboard a new system with ease.

  1. The proof is in the production

Time series databases are not a new tool. However, the ever-growing volume, velocity, and variety of data, and the pressure to create rapid insights and actions from it, means many technologies have not been properly tested within the wider market. Business decision-makers need to be looking for software where there are robust use cases and clear examples of return on investment.

Data is everchanging, and businesses need to recognize this and evolve to keep up. It is now an independent asset with its own C-Level owners, allowing businesses to automate decisions in fields including, but not limited to trading, production, and network. Plus, it holds a higher cost value, as businesses are now finding they pay more for it, but it can then contribute greater economic freedom to businesses than ever before. Additionally, data has become fast-paced in how quickly it can affect markets and created in type, as unstructured and structured data find themselves grouped together more and more.

There are many benefits businesses can reap from continuous, context-rich data analytics-driven insights based on time-series data and so these market changes must be recognized, and business activities adjusted accordingly. Real-time analytics using time series data can deliver greater business decisions, enable enterprises to react faster to market changes, increase customer satisfaction, and improve their bottom line. But only if business leaders step up and ensure their teams have the right technology in place at the right time.

About the Author

James Corcoran is Head of Growth at KX. KX is the fastest, best-informed, real-time decision-making engine in the world. Capable of capturing any data, from any location and in any format, this unrivalled streaming analytics technology drives the most demanding business decisions with real-time continuous intelligence. Widely adopted throughout the financial industry, where it is employed across a range of data-intensive arenas, KX is also deployed in industries as diverse as manufacturing, automotive, energy, utilities and telecommunications.

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