Cloud-based analytics are soaring in popularity. Enterprises transition from on-premises to the cloud for easy scalability, flexibility, and cost-effectiveness. But cloud analytics comes with its fair share of challenges. This tech blog for IT professionals lists the key challenges with cloud analytics and how to overcome them.

CHALLENGE #1: Getting On-Premises Data into the Cloud

Analytics is only as good as the data fed into the engine. The cloud eradicates data silos and feeds the analytic engine with live, complete data. But getting to such a state is hard for most enterprises.

As digitization continues unabated, enterprises grapple with increasing volumes and velocity of data. Enterprise data comes in different formats and patterns, such as Excel sheets, SQL databases, PDFs, JPGs, and more. Several data sources are incomplete or inconsistent or have ambiguous sources of truth.

Most enterprises struggle with their data architecture. They remain incapable of handling data on an exponential scale and end up with diverse tools to load different data types. The data loading process is often complex and time-consuming. Limited bandwidth connection makes it worse.

To move data into the cloud-based analytics tools in a viable way,

• Structure, cleanse, and order all data sources upfront. This eliminates flab and ensures optimal use of cloud and bandwidth resources.
• Analyze the infrastructure and develop a strategy that works well with the infrastructure. Working within one’s limitations is far more effective than failing spectacularly. Not understanding the cloud environment properly also leads to cloud sprawl.
• Ensure visibility into the migration process and fix accountability. Adopt a structured, methodological approach to data migration.
• Automate the process. Automation resolves the most common issues and speeds up the process.

CHALLENGE #2: Security and Control over the Data

Cloud security has improved leaps and bounds over the years. Cloud providers deploy advanced security systems to secure their platforms. But issues persist.

The biggest security worry stems from losing control over the data. Most enterprises have sizable personal identifiable data (PII) data and trade secrets on-premises. Operational exigencies and regulatory mandates leave such critical data behind private networks. Exposing such sensitive data to the cloud analytics platform opens the door for misuse.

Many enterprises have multiple cloud accounts. Storing and exchanging keys is another big point of security concern.

As countermeasures,

• Vet cloud platforms to accept robust models. Designing the analytics environment from the ground up will not always be viable.
• Develop a strong data governance policy that clarifies the appropriate use of data.
• Have a robust policy on PII. One option is to migrate only the IDs for the analysis. Take the IDs back to on-premises post-analytics, for translation.
• Use cloud-enabled sandboxes for trial-and-error of new ideas. Create a prototype of the analytics environment. Resolve the security glitches and concerns in the sandbox.
• Have clarity on data storage, encryption, and compliance protocols in use.

CHALLENGE #3: Adapting to Rapid Changes

Technology is always in a state of flux. Business models also evolve rapidly, to cope with tech changes, and to satiate fickle customer sentiments.

To stay current, enterprise IT teams have to rebuild their analytics infrastructure from time to time. For instance, they may have to add data sources or tables to operational databases, as business needs change. But in most enterprises, data platforms evolve reactively. By the time upgrades become operational, they might have become obsolete.

Traditional methods of modifying data pipelines are slow, complex, and error-prone. The latest machine learning models offer unlimited potential. But it is impossible to apply such models quickly. Putting models into production is a lengthy process that involves several agreements. Changes in business conditions during this time may influence the efficiency of the model. Also, the quality of the data may be low for the model to deliver reliable results.

Cloud analytics requires different architecture and approaches compared to traditional on-premises batch analysis. Leading enterprises:

• Use services such as Azure ML that enable creating multiple machine learning models with a single click.
• Leverage open, containerized analytics architectures that make analytics capabilities more compostable. Such a model allows enterprises to create flexible apps and connect insights to actions in real-time.
• Do not implement ready-made solutions blindly. They tweak the model to factor in custom problems.
• Invest in automation. Automation manages the metadata across the data estate, sparing the need to exchange complex code manually. Development teams get easy drag-and-drop interfaces to build ETL pipelines.
• Use Power BI or similar dashboards to understand what is happening “under the hood” of the machine learning process. Without such monitoring, the results may be out-of-context. Business decisions based on such insights may become counterproductive.

CHALLENGE #4: Cost Overruns

The cloud is cost-effective, but only when done right.

The cloud eliminates expenses related to on-premises storage systems. But cloud costs may go out of control. Some cloud vendors force a lock-in, negating downward scale, one of the key reasons to migrate to the cloud. Even without such lock-in, many enterprises leave cloud resources and applications running. Such carelessness drains money.

• Shop around for the right solution that addresses existing analytics requirements. Make sure the vendor offers flexibility to scale up as needed. Avoid vendors who demand lock-in.
• Enforce tight control over the creation of cloud accounts. Centralize the process. Make individuals and groups go through a formal request process for cloud-based resources.
• Enforce transparency on the consumption of cloud resources. Migrate all cloud accounts under a single ‘master’ account.

Here are five additional tips on how to save money on cloud deployments.

CHALLENGE #5: Access to Talent

Setting up cloud analytics needs diverse architectures and approaches. Enterprises have to re-tool their applications and systems and re-architect them for the cloud.

Resources such as Azure Data Lake are inexpensive storage options in the cloud and offer easy scalability. But using such resources necessitates adapting complex data lake concepts. Likewise, setting up point-to-site VPNs for high-security data needs complex network configurations.

Expertise for such tasks may not always be available. Hiring them full-time may be expensive, depending on the skills required.

As solutions,

• Use tools that shift data without the need to refactor applications for the cloud. Several ready-made tools enable replicating and extracting data across multiple environments.
• Partner with consultants. Allow internal hands to gain experience from such outside experts. Develop a knowledge base to document the processes.
• Explore outsourcing to access talent at scale.

The global cloud analytics market will touch $5.4 billion in 2025, up from $ 23.2 billion in 2020, with a CAGR of 23.0%. But only enterprises that can overcome the challenges reap the benefits.