One size does not fit all
What is Data Science?
Data Science uses tools, algorithms and machine learning to discover hidden patterns in raw data. Data Science is the advanced stage of data analytics where algorithms are written to train machine learning (ML) and artificial intelligence (AI) models to unearth patterns and act on business insights. Companies are leveraging Data Science in different forms to drive business insights for making informed decisions and create digital solutions for their customers.
Data science falls under the following majors ranging from Computer science, Mathematics, Chemistry, Psychology and Economics. This is usually because data science can be applied to solve problems across many disciplines. Data science involves the delivery of information gleaned from advanced analytics applications run by data scientists for business use.
Challenges in Data Science
While working on data science, some of the common challenges we encounter are:
- Analysing huge volumes of data from disparate sources
- Availability of data for analytics in recognizable patterns for tools or machine learning algorithms
- Overlaying business context and process to the patterns to ensure relevant analysis to deliver business insights
- Improving the time-to-value of the data science process
Tibil’s Data Science Solution
Our data scientists at Tibil have the right blend of statistical, technological and business skills to detect patterns in messy unstructured data to generate actionable insights. Our data scientists wear multiple hats in their quest to derive valuable business insights from big data that’s often unstructured, difficult to access and basically messy. They are mathematicians and statisticians when needed, programmers at times, visualizers, analysts, trend spotters and even the ones who communicate with the business and senior management.
Some of the tasks performed by our data scientists are:
- Collecting large amounts of data and analyzing it
- Using data-driven techniques for solving business problems
- Communicating the results to business and IT leaders
- Spotting trends, patterns, and relationships within data
- Converting data into compelling visualizations
- Working with Artificial Intelligence and Machine Learning techniques
- Deploying text analytics and data preparation
Tibil’s expertise in Data Science can be understood more clearly from our Predictive Maintenance Analytics Platform and from a couple of examples in the Real Estate and Utilities sectors.
TIBIL’s Predictive Maintenance Analytics Platform is used to model, simulate, test and deploy our predictive maintenance analytics solution. Predictive analytics models are applied to the prepared data to identify patterns and derive insights in the form of dashboards and alerts. The output helps in identifying the root cause analysis and in determining the most optimal corrective action to be taken. Our solution accesses multiple data sources in real time to predict patterns and failures before they occur so that our manufacturing customers can avoid costly downtimes, reduce breakdown and maintenance costs, improve operational efficiency and improve demand forecasting.
Examples of Data Science Implementation
Tibil’s Data Science solution played a key role in developing algorithms and machine learning for a leading real estate company in North America that was focused on enabling High Net Worth Individuals, Developers and PE Funds to navigate the complexities of buying and selling property with agility & transparency. The platform built for the customer handled all the data processing, including cleaning and feature engineering.
Some of the Data science and analysis involved:
- Developing a weighted average of quality values to assign scores for each lifestyle
- Matching algorithm to match buyer with property
- A home valuation algorithm
Tibil is supporting a Mobile Tower management company with their requirement to predict power outages using data science. Our data scientists use past outage data to discover periodic patterns, and consequently predict the outage points and duration of power outages for a BTS station. Past data about power consumption and outages is given to the Energy Management algorithm. The analytics engine creates a model consisting of mixture of individual regression. The statistical model is then applied as a software service which gives forecasts for power outages.
Benefits of Business Intelligence and Data Science in Business
We believe that by leveraging our Data Science solution, our customers can benefit with:
- Business value: Driving business insights by overlaying business context and Tibil’s domain expertise to the analytics process to ensure the true value of data science is derived
- Improved return on investments: Ensuring the right tools and technologies as well as best practice frameworks are used to keep costs down for better RoI
- Accelerated time-to-market: Leveraging Tibil’s decade-plus experience, expertise and technology know-how to deliver insights, innovation or products faster to market
Besides Data Science, we have expertise in Data Operations solution to audit data for veracity, completeness and reliability, Data Engineering solution to help you prepare and normalize data and build data lakes, Feature Engineering solution to help transform the prepared data into derived variables and formats for data analytics algorithms and models, Data Analytics solution to analyse data using statistical models to generate dashboards and reports, and Data Maturity Assessment to help you baseline your current data posture.
