How To Boost Efficiency With Real-Time Data Analysis

How To Boost Efficiency With Real-Time Data Analysis

Like most things in this world, data expires. With the speed that new data gets generated, it’s imperative that organisations utilise the latest information, process as they arrive, and we are talking about processing times of milliseconds, to make decisions.

Otherwise, organisations risk operating on outdated assumptions. 

There’s a shift in business intelligence. Companies are modernising their data infrastructure to meet the real-time demands — its systems continually collect, process, and analyse incoming data, a big departure from a time when data was processed in batches. This continual process has a significant impact on an organisation’s ability to compete, understand customer trends, and address market changes in a timely fashion.

Of late, many moved to the cloud since the way organisations operate now requires new data infrastructures to manage data collection, processing, and analysis at scale. Cloud data warehouses and data lakes offer centralised cloud data stores, computing power, and the flexibility needed to analyse data in real-time.

To build fast data architectures that can do millisecond processing, organisations need to use systems that deliver timely and cost-efficient data processing focused on developer productivity. It’s imperative for a fast data architecture to have:

  • Performant and reliable data acquisition or ingestion
  • Flexible storage and querying
  • Sophisticated analysis tools

Also, real-time data processing has changed data centre operations, so data centre management has become a key tool in data architecture that meets the demands of real-time decision making.

Boosting efficiency

Trends change quickly, and if organisations use last month’s data to diagnose a current problem or inform the next big decision, they can miss opportunities. 

Real-time data analytics help companies to provide a personalised, customer-centric experience, giving an insight into their changing behaviour and improving the efficiency of marketing. 

Typical industries that rely on real-time data analytics include information technology, financial services, transportation, healthcare, and marketing. In these scenarios, data must be understood immediately to steer decisions, and in some cases, even deliver products or services to customers.

By 2022, Gartner suggests, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Here are some ways organisations can boost efficiency with real-time analytics:

Also Read: Real-time Analytics, What Marketers Should Know

Tracking from the get-go

When you put out a new product release, and you want to know how it performs, real-time data analysis enables you to QA your analytics tracking from the get-go, so you can minimise the risk of losing precious data. The measurements taken by metrics and the insights provided by analytics enable product teams to make informed decisions about upgrading product functionality or adding capabilities. 

Without measuring and analysing the results, they would have no idea if the revisions implemented are effective or even necessary. Product analytics can inform team members about which features are working and which are not by creating a reliable road-map that assesses the product at all stages.

Real-time data analytics help a brand to understand why consumers are buying its product and how they are using it. Some businesses can take this a step further to be able to find out why their consumers might opt for a competitive product instead of theirs.

Real-time data analysis helps to immediately spot opportunities to double down on campaigns and/or product features that work and analyse customer journeys in seconds — you can know immediately what’s working and what’s not, so you can iterate efficiently.  

Up-To-Date Data

Traditional business intelligence is geared towards analytical reporting. Organisations need operational reporting that is real-time in nature but do not have the ability to wait for a data refresh during critical business times like the financial close process and monitoring the supply chain. Real-time data analytics allows organisations to present real-time transactional information side-by-side with historical information in a single analytical environment. Get up-to-the-minute reporting of key data metrics while maintaining high-performance levels for operational processing.

Real-time dashboards enable organisations to monitor the supply chain or quickly analyse financial health throughout the enterprise. For KPI, pre-build the analyses and use cases you track on a regular basis and store them in dashboards or bookmarks. With big data integration, these dashboards update on their own to give you the most up-to-date data available.

In recent years, the supply chain industry has seen dramatic improvements due to the application of real-time insights. Freight providers use real-time information to understand shipping trends, cut costs by eliminating inefficient routes, and deliver improved customer experiences.

In the financial services industry as well, real-time data analysis not just improves customer offerings, but helps in trading strategies as well, allowing firms to react to the latest market trends.

Also Read: Is AI Disrupting BI?

Key to mitigating risks

Attacks are getting increasingly sophisticated and severe, leaving organisations scrambling to repair the damage. Many talk about how it takes businesses too long to notice cyber threats, and when they do find evidence of infiltrations, it often takes longer to fix things since hackers had a prolonged period in which to wreak havoc.

Many have turned to Security Information and Event Management Software that rely on real-time data to aggregate and analyse activity from data sources across the entire IT infrastructure. Analysts suggest that real-time data could address some of cyber security’s weak spots, as it gets delivered to end-users immediately after collection. That characteristic alone means IT professionals could receive up-to-date insights about the security of a network or related topics, and promptly act on them.

A 2019 report published by CrowdStrike found that respondents take an average of 162 hours to detect and contain breaches. Furthermore, 80 per cent of respondents said they were unable to stop attacks on their networks over the previous 12 months, and 44 per cent cited slow detection as the cause. If security teams have access to real-time data when needed, such visibility solves the problem of not detecting breaches quickly enough. Thus, companies save time and money, since access to the network does not remain open to malicious parties for as long as it may once have.

Helps to lower costs, drive better decision-making

Not long ago, big data used to require extensive mathematical understanding and IT support, now businesses can leverage the benefits of real-time data analytics to lower the costs of hiring coding experts to take advantage of business data, reduce bottlenecks within processes and ensure team members have what to pull insights from the data. 

One of the biggest benefits of real-time data analytics is the ability to move forward on both small and big decisions in a timely and productive manner. Through accurate insights, you can strip, update and introduce new business ideas and processes to your organisation with little risk as the analytics provides you with all the necessary information. 

The majority of the time, real-time data analysis can help you avoid serious events and non-conformances by notifying you before a problem occurs. In the event of an unforeseeable problem, such as an equipment malfunction, real-time data analytics helps companies react quickly and mitigate the damage. When you consider how timely, better decision-making can help your business, it’s clear that real-time data analytics technology is well worth the investment.