Treselle Systems, Inc: Helping customers conquer Big Data Challenges
Treselle Systems has rich experience in building products in the big data space. According to the company Strategy & Roadmap, Architecture & Technology, Data Variety, Disparate Data Sources, Disparate Data Stores, Disparate Data Delivery, Mining Data, Analytics, Talent Gap and Data Governance are of the most common challenges before Big Data customers. Finding talent who can manage, process, and create insights from data from both software as well as analytic perspectives seems to be a big issue for the companies. As a client-centric organization, Treselle Systems consistently strives to understand its client’s needs as well as stays close to observing industry trends in the big data space. This has allowed the company to develop, hone, and systematize a set of practices that has evolved as some of their core competencies.
The company is led by Sudharsan (Suda) Madabusi, President &CEO.The challenges with volume and velocity are somewhat solvable with technologies such as Hadoop, Storm, Spark, and others. However, the industry has a dearth of technologies to address the variety problem. This motivated Treselle to develop expertise in ingestion, cleansing, transforming, and to perform analysis on variety of data that ranges from complex energy drilling well data, social data, SEC filings, retail & geospatial data, real estate, banks, healthcare, pharmaceutical, micro & macro economy data, public & government data like Census, SNL, BLS and many others.
Technology evaluation and recommendation is one of the key successes to any Big Data initiative. Hadoop is not a magic wand and may not help much if the use case has variety challenge. Treselle has developed expertise in analyzing a client situation and recommending proper technologies for the problem at hand.
Treselle has developed expertise in ingesting variety of data in myriad formats from a multitude of delivery methods that is not easily possible with existing tools and techniques. Some of the ingesting mechanisms include complex web scraping at multiple depth levels and interactions to get the data in raw format, from mainframe systems in EBCDIC format, raw Oracle dumps, and others to name a few. Treselle has developed expertise in machine learning technologies like Mahout, Spark MLlib, R, and Python. Treselle is capable of performing different clustering, association rules, finding patterns, classification, time series analysis, regression analysis, forecasting, predictive analysis, building models, offering recommendations, and many others on structured, unstructured and semi-structured data.
The company realized way ahead that when entering an emerging new field such as Big Data, it is prudent to focus on areas that align with their core competencies, develop expertise in these, and add other adjacencies as they grow. This has helped Treselle immensely as they keep the pulse on the market and evolve as well as add value to client engagements as they bring forth skills in multiple segments that are very relevant to their target market.
In addition to having clarity on the technologies and market segment focus, Treselle also built full-stack teams necessary to execute multi-year, complex Big Data products. The company has expertise in developing not only projects but also multi-year SaaS based complete products for Silicon Valley startups. This requires deep research capabilities and expertise in giving product direction/roadmap and not expecting clients to provide detailed product requirements.
The company is well positioned to take advantage of the opportunities in emerging technology areas such as Big Data, Cloud Computing, and Mobile. They are gearing towards expanding business by 300% in the next 12 months. “The sales pipeline is strong and we are expanding within US as well as establishing our presence in Europe and Asia”, concludes Sudharsan.