Big data is not a novel notion; it refers to complex datasets that are too dense for conventional computing configurations to handle. The extent to which data scientists, data engineers, and data analysts can manage, explore, and analyse this gold mine of unprocessed business information, however, is new—or at least still evolving.
We can do more with big data in 2024 than ever before because of the general shift to the cloud, new methods of processing data, and advancements in artificial intelligence. Nevertheless, given the accelerated pace at which data is generated and combined throughout the organisation, will our analytical capacities grow quickly enough to deliver significant insights on schedule?
How the future of big data analytics is being affected by new procedures and technical advancements
The Increasing Velocity of Big Data Analytics
Gone are the days of exporting data once a week or once a month and then sitting down to analyse it. Big data analytics will eventually place more emphasis on the freshness of the data in order to achieve real-time analysis, which will boost competitiveness and allow for more informed decision-making.
Streaming data, as opposed to batch processing, is crucial for real-time insight, but it has drawbacks for preserving data quality. Acting on incomplete or inaccurate data increases with fresher data, a risk that can be mitigated with the help of data observability principles.
For instance, Snowflake revealed Snowpipe streaming during the summit this year. The business has redesigned their Kafka connector so that data enters Snowflake instantly and can be queried, resulting in a tenfold reduction in latency.
Google recently announced the debut of Dataflow Prime, an enhanced version of their managed streaming analytics service, and the ability for PubSub to stream straight into BigQuery. To help add more metadata, structure, and control to data assets, Databricks has introduced Unity Catalogue on the data lake side.
Real Time Data Insights
Some people may think that having access to real-time data for analysis is unnecessary, but that is no longer the case. Consider posting your tweets or trading Bitcoin based on last week’s price or trending topics from a month ago.
Real-time knowledge has already caused a stir in social media and banking, but its effects go far beyond these sectors: For instance, Walmart constructed what might be the biggest hybrid cloud in the world to handle their supply chains and provide real-time sales analysis.
Real Time Automated Decision Making
Artificial intelligence (AI) and machine learning (ML) are already being successfully used in sectors such as manufacturing, where intelligent systems track wear and tear on parts, and healthcare, where they are used for detection and diagnosis. The assembly line may be automatically diverted to another location by the system when a part is about to fail in order to allow for repair.
That’s a real-world example, but there are a tonne of other uses as well. For instance, email marketing software may identify the A/B test winner and apply that information to future emails, or customer data analysis can be used to assess loan eligibility. Of course, companies can always have a final manual approval stage if they don’t feel comfortable automating choices just yet.
The Heightened Veracity of Big Data Analytics
It gets harder to guarantee the quality and accuracy of the data we get. Check out our previous piece on the future of data management to learn more about this; for now, though, let’s discuss some trends pertaining to the validity of big data analytics.
Data Quality
It’s usually a smart business decision to make decisions based on data—that is, unless the data is flawed. Bad data is also information that is erroneous, incorrect, incomplete, or disregards context. Thankfully, a lot of data analytics tools can now recognise and highlight data that doesn’t seem to belong.
Naturally, diagnosing an issue is always preferable to treating a symptom. Rather than depending just on tools to detect inaccurate data in the dashboard, companies should thoroughly examine their pipelines from beginning to end. Determining which source(s) to use for a particular use case, how to analyse it, who is utilising it, and other factors will lead to healthier data overall and should minimise problems with data outages.
Data Observability
Observability is not limited to pipeline failure detection and monitoring. To have control over the quality of their data and make improvements, organisations must first grasp the five pillars of data observability: data freshness, schema, volume, distribution, and lineage.
Furthermore, to draw attention to problems with data quality and discoverability (as well as possible problems), an automated monitoring, alerting, lineage, and triaging system such as Monte Carlo can be used. The ultimate objective is to completely eradicate erroneous data and stop it from happening again.
Data Governance
It becomes even more crucial to take appropriate precautions while dealing with the amounts of data at hand. In addition to the fact that following laws like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) is essential to avoiding fines, data breaches can seriously harm a company’s reputation and brand.
Although we’ve written about data discovery before, it bears repeating here: real-time insights about data across domains while adhering to a central set of governance norms.
Establishing and carrying out a data certification programme is one approach to guarantee that every department in a company uses data that complies with relevant and established standards. Furthermore, data catalogues can be used to specify the permissible and impermissible uses of data by stakeholders.
Storage and Analytics Platforms are Handling Larger Volumes of Data
Things like processing power and storage availability can be practically endless when utilising cloud technologies. Businesses may use the cloud to grow to whatever degree they require at that particular moment, so they no longer have to worry about purchasing additional machines or physical storage.
Furthermore, the utilisation of cloud data processing eliminates delays and obstacles by enabling simultaneous access to the same data by numerous stakeholders. It also implies that current data can be viewed from anywhere at any time, provided the proper security mechanisms are in place.
Data warehousing is the present state of affairs in this regard, with the three most prominent providers—Snowflake, Redshift, and BigQuery—functioning on the cloud. In other places, Databricks combines parts of data lakes and warehouses with its “data lakehouse.”
However, the core objective is still the same: data, analysis, and possibly artificial intelligence in one or a few locations. Naturally, as data volumes increase, there is a growing demand for more and improved methods of handling, organising, and presenting these massive data sets in a comprehensible manner.
Aware of this requirement, dashboarding is becoming a more important feature of contemporary business intelligence solutions (Tableau, Domo, Zoho Analytics, etc.), which make it easier to handle and track massive amounts of data and support data-driven choices.
Processing Data Variety is Easier
Larger data quantities usually correspond with a greater variety of data sources. It is nearly hard to manage all these distinct formats manually and still achieve any kind of consistency. Unless your team is extremely big and enjoys doing menial work.
More than 160 data source connectors, ranging from marketing analytics to finance and operations analytics, are included into tools like Fivetran. Reliable data pipelines can be created by applying prebuilt (or custom) transformations to data that is pulled from hundreds of sources.
Similar to this, Snowflake has integrated ML and AI capabilities into their data platform by partnering with businesses like Qubole, a cloud big data-as-a-service provider. This means that when ‘X’ data is imported into Snowflake with the appropriate training data, ‘Y’ will occur within the platform.
Fortunately, rather than trying to compel consistency before data is loaded where it needs to be, the focus of big data analytics right now is very much on finding methods to aggregate data from many sources and find ways to use it together.
Be Ready to embrace the Future of Big Data Analytics
The future of big data analytics is no longer bounded by cost restrictions, even if many large organisations are already adopting all or most of these trends, providing them a competitive advantage.
Without needing the resources of a Fortune 500 company, data scientists and engineers are creating novel approaches to reveal insights buried behind mountains of data.
Big data analytics will be a part of business plans for a growing number of small and mid-sized businesses. Those who take steps to comprehend and welcome the future will find it to be bright. So opt for data analytics training in Indore, Pune, Jaipur and Patna to ace this skill.
