Much has been said about big data, the volume, velocity and variety, and what businesses can achieve by harnessing it. But, we often hear about the “pain points,” those specific problems that businesses face on a day-to-day basis when it comes to big data — as well as issues that they may encounter while using technologies and services.
Businesses have made significant investments in analytics tools and processes. There’s no shortage of technology, and there are abundant vendors, but what is crucial is connecting technologies and workflows to get the best out of the big data.
Here are five common problems that arise in data infrastructure and how you can solve them.
Dealing with the impact of Cloud migration
Cloud is an ideal target for data consolidation, self-service data practices, and data sharing across organisational boundaries. But migrating isn’t a simple process. There’s an alarming misconception that the business can carry the same infrastructure perspective into the cloud, treating it as just another data centre. Migrating should be planned carefully for the complexity, time, business disruption, risks, and costs.
Cloud migration means redirecting the access of business processes, groups of warehouse end-users, reports, applications, analysts, developers, and data management solutions.
Your plan should explain when and how each entity will be migrated or redirected to the cloud.
Complicated workflows and system integrations need to be rewritten. Cloud software vendors will need to focus on building bridges and interfaces that will enable transition with minimal disruption, meaning more APIs and fewer ODBC connections.
Cost is a big challenge. According to experts, an awareness of cost into all actions will empower the IT teams to cover all the bases when undertaking IT improvements. The challenge is to create a light touch approval process that keeps things moving, rather than an ITIL process.
Also, clouds experience the same threats as a traditional data centre environment because cloud computing runs software, which has vulnerabilities, and hackers exploit those vulnerabilities. Since cloud storage is accessible from anywhere with an Internet connection, new, sophisticated security challenges have come to the fore. Big data is collected from multiple sources, and it is imperative to make sure that the data coming in is secured. Big data can be manipulated at the time of processing mainly because tools like Hadoop and NoSQL were not originally designed with security in mind.
However, unlike information technology systems in a traditional data centre, in cloud computing, responsibility for mitigating the risks that result from these software vulnerabilities is shared between the cloud service provider (CSP) and the cloud consumer. It’s important for businesses to understand the division of responsibilities and trust that the CSP meets their responsibilities.
Many times, the internal resources within the enterprise do not have the expertise on how things work in the cloud. Organisations should look beyond the migration phase and concentrate far more effort on how they “operate” on the cloud. At a practical level, notifications, reports and budgeting tools help monitor spend, along with training to understand how to work in the cloud and pick up on anomalies.
IBM Cloud Paks for Automation, an AI-powered portfolio, is an option to consider as it helps customers streamline business processes, automate tasks based on data analysis and continuously improve workflows that run centrally, in networks, and at the edge.
Also Read: Leaving A Legacy of Cloud
Distributed teams and local performance needs
Usually, companies do not build a single data processing cluster in a centralised data centre for their big data teams spread worldwide. Building such a cluster in one location has Data Recovery implications, not to mention latency and country-specific data regulation challenges. Typically, companies want to build out separate local clusters based on location, type of application, data locality requirements, and the need for separate development, test, and production environments.
But having a central management is crucial for operational efficiency, simplifying deployment, and upgrading these clusters. Having strict isolation and role-based access control (RBAC) is often a security requirement. Experts recommend IT administrators to implement a central way to manage diverse infrastructures in multiple sites, with the ability to deploy and manage multiple data processing clusters within those sites. Access rights to each of these environments should be managed through strict BU-level and Project-level RBAC and security.
Also Read: How Will Businesses Manage Data Deluge in 2021?
Handling large amounts of data with accuracy
There are 2.5 quintillion bytes of data produced every day from cell phones, sensors, social media, websites and even online transactions. Because of the deluge of data, a major challenge is to process data in real-time, in a cheap and fast manner.
The right tool to analyse the big data with efficiency is Hadoop, designed by Apache software, which has a function called MapReduce that reduces the whole data into smaller and more readable fragments.The software then processes each fragment with the means of the single node of a cluster. The Infosphere BigInsight, built by IBM, and Cloudera are also effective big data analytics tools. It can help a business meet core requirements while maintaining the compatibility of the data. Also, data cleansing is imperative to ensure data analysis is centred around the highest quality, most current, complete, and relevant data. If your data is clean, well-organised, and free of silos but still isn’t making any sense, the next step is to segment your data for a more detailed and focused analysis. Consider what you’re trying to achieve from data analysis and what specific questions you want to answer.
Automating data wrangling
The process of manually converting or mapping data from one raw form into another format that allows for more convenient consumption of the data with the help of semi-automated tools is a massive chore in the big data world, which wants to combine structured and unstructured data from myriad sources and is anything but orderly when the data first arrives in raw form. It’s said that data scientists spend from 50 per cent to 80 per cent of their time mired in this mundane labour of collecting and preparing unruly digital data before it can be explored for useful nuggets.
Data visualisation is crucial in interpreting the data. However, to perform data visualisation, the data must first be understood in a context. For example, if the data is sourced from social media, then it is necessary to understand or decipher first the customer needs. Only then can you present the data in a more understandable format. Moving to Hadoop-generated data and using a software like Trifacta and then feeding the data into Tableau, which has a variety of options available, including a desktop app, server and hosted online versions, and a free public option, data prep time could be reduced by 70 per cent. The end results for the company could be faster time to value for the information and faster reaction time for forecast adjustments.
There are also hundreds of data import options available, from CSV files to Google Ads and Analytics data to Salesforce data.
Also Read: Marketing With Data Lakes and Data Warehouses
Data warehouse transformation is hard work
Data warehouses are big and complex systems that can store terabytes of data that business leaders depend on to make important decisions.Tinkering with such an integral part of a business makes the best of the CIOs jittery.
Start with discussions on conforming, or merging, data sets to bridge the gap between technical and business users and an ongoing relationship between IT and business sponsors. The need for communication will never go away.
There are two approaches to simplifying a data warehouse transformation: low-code, visual solutions that require minimal SQL knowledge, and code-acceleration solutions like DBT that essentially write code behind the scenes to accelerate development.
As those two approaches develop, companies will be able to match the skills and work patterns of their data teams to the right software package.
Beyond the instantaneous scalability of the cloud, there are other approaches one can take to address the optimisation conundrum. The most effective is to remove the inherent burden of ETL processes, commonly referred to as Offloading ETL. Utilising Talend Big Data and Apache Spark, IT can work with business analysts to perform pre-load analytics in a fraction of the time of standard ETL. Not only does this give business users insight into the quality of the data before it is loaded into the warehouse, it also allows IT a security checkpoint to prevent poor data from corrupting the warehouse.
Resolving the pain points related to big data upfront allows businesses to realise meaningful insights, experience game-changing innovations and ensure ROI for their big data investments.
