Efficient and Reliable Enterprise Data Management
What is Data Engineering or Data Management?
Data Engineering is the use of scientific principles to design and build machines, structures, and other items, including data pipelines and data lakes or data repositories. Data engineers scientifically set up and operate a company’s data infrastructure preparing it for analysis by data analysts and scientists. Data engineering is the aspect of data science that mainly focuses on the practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. This is where data engineering steps in.
Types of Data Engineering Services
Master Data Management
Master data management is the implementation of one single master reference source for all business-critical data in an organization. MDM leads to fewer data-related issues and improved business processes
Enterprise Data Management
Enterprise Data Management is the process of accurately defining, effortlessly integrating and seamlessly retrieving data for both internal business processes and customer communication. EDM’s main focus is creation of accurate, reliable, verifiable and consistent data.Data Lifecycle Management
Data Lifecycle Management is a policy-driven approach that can be automated to take data through its useful life. It is the process that can be defined and institutionalized to manage data right from its inception to the end of its useful life.Customer Data Management
Customer Data Management is the process adopted by businesses to process and track their customer information throughout and beyond the course of an engagement. The data can be efficiently accessed and used by enterprises using various solutions to my customer information and proactively seek customer feedback.Common Challenges in Data Lifecycle Management
While working on data, some of the common challenges we encounter are:- Multiple data sources with no single source of truth
- Inaccessibility of data – the data is on multiple systems that are not accessible
- Scale of data – humongous volume of data deters companies from embarking on an analytics exercise
- Messy data that’s not easily available for analysis and usually is incomplete or inaccurate
- None of the data sources are integrated in one place for easy access
Tibil’s Data Engineering Solution
Tibil’s core competency is data engineering – in building a strong data foundation for analytics. Our data engineers tackle the influx of huge volumes of structured and unstructured data that are relevant for the function or business. This step is the foundation of any data strategy. If one were to imagine the whole process as a pipeline, if the first step (valve) that is data engineering, is not executed right, it will create a domino effect the overflows into the outflow from the pipeline.
From our experience, we can categorically state that before data can be analyzed and leveraged with predictive methods, it needs to be organized and cleaned. Our Data Engineers begin this process by making a list of what data is stored, called a data schema. Then, they choose reliable easily accessible locations for storing the data – data warehouses, data lakes and data marts. Data Engineers create noETL (Extract, Transform and Load) processes and pipelines to feed the data into the data warehouse or lake.
Machine learning and data mining are the technologies that are widely used by our data engineers. Tools like R, Python, and SAS are used to analyze data in powerful ways. Our Data Engineering solution uses tools like SQL, NoSQL and Python to make data ready for data scientists.
TIBIL’s Data Engineering solution covers the entire spectrum of:
- Validating existing datasets & sources and verifying data quality
- Integrating and organizing data from disparate source
- Cleaning and filtering the data to ensure irrelevant data points are cleared and missing data sets are discovered
- Preparing the data for transforming into formats that data analytics teams can act on
In addition to getting data from disparate sources, our customers need to process the data in a timely manner in order to make real-time decisions. Tibil’s Data Engineering basically ensures an efficient data flow, often implemented by building data pipelines and data lakes with new technologies that can run and scale in the cloud. Good models, good machine learning and good AI are impossible without well-governed data pipelines in place.
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What Makes Tibil the Best Data Engineering Consultant?
Tibil’s engineers help in aggregating this data (which are present in different environments and collected from different sources), cleaning, normalizing and preparing it for transformation into meaningful and relevant forms. This outcome of audited and prepared data is the first and most essential step in companies making informed and relevant decisions.
Our data engineers ensure that there is no impact to the production environment of our customers by creating a parallel data gathering and analysis environment. They also leverage the latest tools and technologies such as cloud and open source technologies to store and analyze data.
For a Leading Manufacturer of Construction Equipment, Tibil delivered an enhanced data engineering and analytics solution for better demand forecasting, supplier performance monitoring, and production optimization. Some of the business insights from our analysis were:
- Large order frequency opportunity identification and optimal dealer onboarding
- 12 months demand forecast for various geographies with 72% accuracy
- Inventory planning parameters such as EOQ, safety stock, months of supply for all parts
- Classification/ranking of critical suppliers based on performance consistency and trends
Benefits of Hiring a Data Engineering Services Company
We believe that by leveraging our Data Engineering solution, our customers can benefit with:- Single source of truth – a data lake or warehouse where they can find all the data they need
- Scale –engineering the data for future scaling up requirements
- Integration – with various processes and data sources to ensure one place where all data resides
- Volume – ability to seamlessly handle the huge volume and variety of data
- Accuracy – ensuring consistency and reliability of the data
