• Did you know unstructured data accounts for 80% of enterprise data? Hence, it is imperative that organizations fortify themselves against data-related obstacles.
  • The State of Data Innovation Report states only 40% of organizations have comprehensive data aggregation and 29% of respondents say that some or all lines of their businesses keep their data separate or hidden.

We’ve entered the Data Age, and there’s no turning back! A recent research shows that by 2025 the amount of data created, taken, copied and used worldwide will be more than 180 zettabytes, up from an estimated 64.2 zettabytes in 2020.

Let’s imagine that your company is worried about keeping up with the rapid expansion of cloud infrastructure, cloud-native applications and edge databases hosted on-premises. In such a case, a perception change would do a world of good! Let’s not fight the trends; rather, use them to our benefit.

Data volume and speed aren’t going away anywhere. Today, businesses that can bank on data intelligence for revenues are winning.

However, this warrants more than just implementing new cloud storage solutions, assembling a patchwork of point solutions for industry-specific research or even collecting extra funding…..

A comprehensive platform that enables businesses to select the data to search when the need arises – intelligently, independent of the data’s location, source or format is necessary to develop a mature data strategy for this new environment.

Adopting complex new systems and procedures with the correct data techniques and perspectives can be an effortless endeavor.

Any modern data strategy must include data tiering as a critical component. Taking a close look at the data being generated and prioritizing it based on – how frequently it will be used, how quickly it needs to be accessed and what business results it drives is necessary for enterprises to manage the data efficiently.

In this blog, let us discover the top five big data challenges and understand why data tiering is the most effective way out.

Diverse data formats and how they are making business insights perplexing

Multiple data formats equal to complexity in the big data environment. Having a wide variety of data at hand leads to its communication problems –  because without the ability to interact with other data, it loses its usefulness and confounds business insights.

At the enterprise end, there is a range of unstructured, semi-structured, and structured data sets and several standard formats within each. Unstructured data offers flexibility in search but necessitates the skill to manage it and the specialized tools that can make sense of it instantly. This is unlike structured data which restricts usage patterns due to its predetermined schema.

Organizations must be ready to handle this situation since unstructured data makes up about 80% of enterprise data.

There is yet another data challenge! The issue of adequately formed versus improperly produced data! Organizations can face hurdles in sorting through all possible combinations of each bespoke application with its non-standard logging format within each format.

The end goal is to extract business value and then make data-driven decisions that advance the business, notwithstanding differences in the gathering, processing and storing of big data.

What is your best chance of getting the most out of big data, regardless of format?

To identify what data exists within your organization, where it is stored, how old it is and how it can be used to solve problems or seize opportunities, a data integration framework should be at the center of every modern data strategy (i.e., use cases).

An integration framework, among other things, can direct your data processing investments and enable you to think about the platforms and solutions that will aid in making sense of the many data formats for business purposes.

You need a platform that can support all types of schemas before starting your framework to standardize the data shape and shorten the time it takes to conduct an inquiry.

Additionally, you want it to be flexible enough to include the most recent features, support its governance processes and consume data from numerous sources.

Data silos preventing distributed data access

When a mature organization has on-premises, multi-cloud or hybrid infrastructures, a range of data silos, it is nearly as variable as the data itself.

Data silos emerge organically in organizations, seemingly in lockstep with growth, as a byproduct of various factors, including aging (legacy) infrastructure, mergers and acquisitions, decentralized technology and tool management, or general miscommunication between teams and departments.

According to the State of Data Innovation report, only 40% of organizations have comprehensive data aggregation and 29% say that some or all lines of businesses keep their own data separate or hidden.

Some tools and silos cater well to highly structured data accessed via similar patterns. In contrast, others are better suited for unstructured data with random access patterns, particularly as data formats evolve.

But this tool sprawl — often with multiple and disjointed data stores — obscures visibility into data lineage, transformations and overall shape, especially across data lakes and warehouses, making unified data access extremely difficult. For organizations, addressing unique regional business needs across data silos remains a challenge, which is compounded considering security and compliance regulations.

Environments that house numerous disparate point solutions also add to the problem, requiring silos that prevent data from being shared across teams.

To resolve these issues, organizations need to find ways to break down these walls so different teams can reap the benefits without multiple extracts, transform and load (ETL) steps or context-switching between various tools, data stores and sources.

Data is only valuable if accessed and used to generate insights and action. Don’t worry about eliminating data silos — let the data live wherever it does.

Instead, prioritize your efforts on gathering data from all the silos by relying on a consolidated data platform to help bridge the divide.

Poor data quality leaves it’s value to be questioned

Random data is typically unclean and noisy; it may be redundant, raw, lack context, poorly explained, reveal personally identifiable information (PII) or include other irregularities or invalid values.

If these flaws are ignored, they can all harm the quality of the analysis. Trifacta research suggests that 60% of IT workers devote at least half of their working hours to data quality assurance, cleansing or preparation.

The preparations and shaping of quality data are prioritized through aggregations, transformations and enrichments so that you can ask better questions about it and provide insightful analytics and insights. If the data is too complicated to read, you can’t use it.

It is crucial to clean and pre-format data from any source or location to reduce costs and ensure the data’s usability for customers.

Organizations ultimately require a centralized method of routing data to and from the numerous data stores they employ while preserving consistency to avoid mismatches and reduce overhead costs associated with data transmission.

How data value and age affect data relevance

In the end, not all data is created equally. The most expensive storage and the most potent analytics tier is typically assigned to all data by default, which is the usual treatment of all data.

However, if you treat your data the same, your firm wastes money and loses out on important insights.

Consideration of the age of the data and the data’s maximum lifespan utility are two factors that might help you reevaluate the relevance of the data. Data quality does not improve with age as a bottle of wine does!

Soon after it is created, data loses some of its value. Aside from that, not every data value decreases equally. The commercial value of information increases with the speed at which it can be collected, analyzed and used; nevertheless, Forrester estimates that between 60% – 73% of all data created in organizations is not leveraged for analytics.

Although data has a time value, this does not necessitate increased data collection by businesses. They should instead weigh the value of data concerning its function as a “leading actor” to support analytics or forensics or as a “supporting character” for compliance.

Organizations can strike the correct balance between cost, performance and feature set, enabling use cases to be handled without breaking the bank by considering the age and utility of data.

IT and security resources drain by rigid cost structures

The cost of the related analytical databases is one of the highest costs most businesses face when working with big data.

These databases typically grow in line with increased storage and computing expenses. Costs rise while productivity declines as firms implement a patchwork of point solutions to fill in the gaps in database capability.

In 2022, Gartner forecasts that expenditure on software will increase by 9.8% to USD 674.9 billion and IT services will increase by 6.8% to USD 1.3 trillion, with security and analytics receiving the lion’s share of an organization’s budget.

Many point tool pricing models, however, have not considered the mismatch. Although cloud solution providers (CSPs) have provided an alternative that incorporates flexible pricing with fungible units across many services, many tools still have pricing that makes it difficult or impossible for users to benefit from economies of scale.

Customers can design data strategies that can change depending on computing and workload by using a data platform that offers them the advantages of this pricing model.

To sum up

Data is currently an organization’s most valuable and strategic asset, as per the realities of the Data Age. You have two options: either invest in a data platform that can shape data during ingestion and read to detect transactions, trends and patterns automatically or spend your time and money transforming, cleaning and making data usable. A multifaceted, value-aligned data tiering strategy reliant on full analytics at each searchable level is necessary for a cohesive data architecture.