Businesses across the globe accumulate data from multiple sources ranging from emails, mobile apps, websites, IoT devices, and more. While business organizations invest significant time and effort in data accumulation, their efforts in extracting meaningful insights from the data needs more attention. This mismatch in the efforts can be attributed to the volume of data overcoming the capacity of the traditional tools they use to store and analyze data.

With an increasing number of business organizations aiming for their ideal big data analytics service strategy, they overlook an important ingredient that makes all the difference, i.e, a meaningful partnership. Here’s why you must focus more on choosing the ideal big data analytics partner in 2022 and beyond.

But before diving into the ocean of reasons why you must partner with an expert, skim through the challenges that may turn your big data analytics endeavors into a barren land.

Understanding the Premise: Navigating the Big Data Services Pitfalls

The endeavor to extract organizational data insights has taken the business world by storm. Unfortunately, the impact of the end goal becomes diluted during the journey. The reasons are the pitfalls that usually catch unsuspecting business organizations looking to benefit from big data off guard.

Here are the 4 most common big data analytics challenges:

  1. Lack of Process Awareness: Big data analytics service providers often leave no stone unturned in understanding the clients' requirements. But businesses are often in dire straits about what they need from data analytics due to a lack of stakeholder synergy. It can lead to a cascading effect that ultimately affects the value of a big data analytics project.
  2. The Toolkit Conundrum: Big data analytics services leverage various tools such as data warehouse integration platforms, visual analysis applications, enterprise-scale clusters, master data management applications, and more. When they fail to make the best choice of tools, the entire operation is destined to falter.
  3. Growth and Scalability: If businesses aren’t prepared to handle their data as it grows in volume, they are usually left with impulsive decisions searching for quick fixes to manage the increasing data load. It often leads companies to make compromises that ultimately affect the scalability and stability of the analytics process.
  4. Data Validation: Most CFOs and CIOs of business organizations invest in big data analytics solutions to get a single source of truth in the form of insights. However, due to multiple stakeholders and the difference of opinions, service providers find themselves at crossroads, wondering which direction to follow that benefits their clients the most. Conflicts regarding the control of data, the data type, and the direction of the entire operations are common roadblocks business leaders must consider.

5 Tips to Choose the Best Big Data Analytics Service Provider

  1. Know the Right Track: The fact that no big data analytics service partner would be counterproductive for a client is a given. Yet, all it takes are frequently changing requirements and bootstrapped budgets to derail big data operations. Big data analytics services are only as useful as the ability of a business organization to leverage its benefits and the capacity of a service provider to keep the client on track. Knowing the right track depends on the service provider’s ability to understand client requirements and pivot application development and data migration strategies without losing the momentum of the project.

  2. Real time’ data Wizards: Real-time big data analytics implies that a large volume of data is processed as soon as it arrives, and the users receive consumable insights without exceeding a pre-determined time for business decision making. In simple words, computer applications and mobile apps can be primed to deliver customized notifications to a user based on their action in the application.

    Real-time big data analytics can introduce behavioral changes in customers as they use apps. Unfortunately, the rabbit hole goes deeper than usual with such applications and requires knowledge of statistical analysis, algorithm development, etc. A partner that can accomplish this has to be well aware of almost all the ‘live analytics platforms currently available such as Amazon’s Kineses.

  3. Partner licensing Advantages: Everyone wants to derive great value for money on their purchases, and this is what it usually comes down to when choosing from a long list of big data analytics service providers. Big data analytics tools and specific approaches to using them dictate a company's cost, efficiency, and scale of big data operations.

    A big data analytics service partner with partner licensing for essential tools such as Amazon, Hadoop, and Azure platforms will offer instant value. Such service partners are also more proficient in leveraging the potential of such tools to create an effective big data services strategy for the client.

  4. Process transparency: Big data analytics companies develop complex mechanisms such as machine learning algorithms and notification engines that usually require hands-on experience in software engineering to understand. But, a ‘transparent’ service partner is usually unequivocal about important aspects such as customer consent management, data storage guidelines, and the types of data they capture from the end customer.

    To foster a healthy professional relationship driven by trust, process transparency must exist between a big data services company and its client. Aspects such as data ethics and control of the organizational data during the implementation process of the analytics engine are important. The establishment of secured communication lines and data encryption are the key processes to consider when evaluating the transparency of big data service providers.

  5. Staffing Flexibility: Most in-house teams handling big data analytics services feel overworked since the entire process is exhaustive. From setting up data warehouses to implementing data exchange protocols through APIs, numerous teams are involved.

    Often collaboration and communication take a hit during the implementation of analytics services with teams chasing ‘go-live’ schedules. A service partner with the agility and flexibility to manage tasks such as establishing analytics engines and integrating them with the dashboard can ease the pressure on in-house teams.

  6. Data Ownership: Big data service providers seek ownership of client data to achieve consistency in enterprise architecture and tool selection. They strive for the accurate and optimized use of technical resources. On the other hand, business owners require ownership of their data to align business operations with its goals and priorities. It ensures that individual business units can address their needs quickly. In this inevitable tug of war, the synergy of business organizations with their big data analytics service provider is tested rigorously. Hence, business leaders must look for flexibility in data ownership when choosing a service provider.

The one who can bend to the changing requirements of a client's goal will add value to a big data project.

Learn more about how Nalashaa can help you with your big data analytics needs. Send us an e-mail at info@nalashaa.com for more information from our big data wizards!