Big Data Strategy

All companies and organizations that aim to support their decision-making with business intelligence technology begin using Big Data applications at some time. Data processing provided by modern-day Big Data systems helps draw actionable business insights from multiple sources and apply Big Data analytics to both structured and unstructured data from various sources and form a holistic business strategy. IT Svit knows how to build infrastructure and workflows to support an efficient Big Data business strategy for your success!

Big Data tools can help augment literally any product or service and add various useful features to provide more value both for customers and for the business. IT Svit has ample experience with implementing bespoke Big Data tools for various products in finances, marketing, telecom, transportation, industry, education, etc. We help provide new features for your customers and more business value for your company!

Minimization of expenses is yet another important component of a Big Data-based business strategy. Machine Learning models deployed to your cloud infrastructure can help reduce expenses on idle servers while ensuring system resilience against peak workloads. Real-time data processing helps make data-driven decisions that provide tangible benefits for any business!

Every business that wants to remain competitive has to utilize all the capabilities provided by modern technology. While the infrastructure layer is nearly the same for any company, the ways we use our Big Data differ a lot. Some businesses just let it go to waste, while some companies have already implemented Big Data analytics solutions that provide data warehousing and processing to store information from multiple data sources. Both structured and unstructured data can be analyzed in real-time to enable various product features and services.

Approximate roadmap to a successful Big Data strategy looks as follows:

• The business decides what kind of Big Data analytics it wants to utilize and where it should be implemented — in customer-facing products, in mission-critical systems or both.
• The company also makes a decision on the approach to obtaining Big Data expertise — hiring it in-house, subscribing to managed Big Data services from their cloud computing provider or working with an IT outsourcing company.
• Should the business choose to look for a reliable Big Data consulting company to outsource the Big Data implementation to, they select it based on the reputation, ratings, customer reviews, technology stack and skill set provided.
• The Big Data strategy provider must assess the existing workflows and infrastructure in place, as well as the product architecture, to determine the best approach to Big Data analytics implementation.
• Sometimes the existing cloud infrastructure must be reorganized to remove the performance bottlenecks and maximize the room for growth capabilities and enable it to integrate with Big Data solutions flawlessly.
• Once the systems are ready to support Big Data processing, a structure for the future Big Data project can be designed, and an appropriate Machine Learning model can be selected.
• The chosen Big Data analytics algorithm must be trained on historical data sets to establish the required patterns in both unstructured and structured data. This includes in-depth data mining and data testing and enables data-driven decision-making
• The trained ML model must be deployed to production and the Big Data processing can begin in earnest to provide business value for your company.

Depending on the expected results, Big Data analytics can be applied to various use cases:

• Product features. IT Svit has deployed a Big Data solution for a cattle breeding concern for monitoring the cattle conditions, detecting various diseases based on symptoms, data normalization and recommending treatment at once. It helped reduce the cattle mortality rate by 30% due to saving time on calling the vet and awaiting for medication prescriptions.
  We have built a customized data search algorithm able to perform data mining, deduplication and content filtering to deliver customized news feeds on niche-specific topics for customer websites.
  We have employed Optical Character Recognition (OCR) to enable data processing of customer photo IDs, which enabled customer verification during registration for a Tokyo-based dating app.
  We have developed a CV parser for our Hurma System product, using OCR to parse data from CVs and resumes in seconds and fill in the appropriate profile fields, saving HRs and recruiters a ton of time on manually updating candidate profiles.
• System performance. Our DevOps team has implemented more than 50 predictive analytics systems, where a Machine Learning algorithm performs real-time data processing, track the normal system performance patterns and identifies any anomalies (module failure, rapid workload growth at the beginning of a DDoS attack, hacking attempts), which could have resulted in major losses. Instead, our Big Data analytics systems apply the response scenarios, ensuring system resilience and enabling self-healing infrastructure, which is essential for business in literally any industry, from marketing and software development to finances, telecom, and banking.

IT Svit knows how to design, implement and support a successful Big Data strategy for your business, which will drive more value in various aspects of your operations. Should you want out assistance with Big Data projects — contact us today, we are always ready to help!