Analytics is Broken. It’s Time for a Change
Spreadsheets are the lifeblood software for most organizations. But for a small (and rapidly growing) population of analysts, there is an unmet need for something more. The pains of managing their data are growing on a daily basis with no real solution in sight. The widening gap between Excel users versus SQL or R programmers is becoming a real problem. The most basic set of tasks of managing rows and columns takes unreasonable amounts of time and involves costly resources. For organizations ignoring these problems, this means either squandering away potential growth opportunities or losing their competitive edge, and in most cases both.
Where career expectations, equality and life balance meet | Our experience in Tech
We write code every day, solve the most complex of issues, and diligently come up with the most simple and sophisticated solutions for every complex problem. We are Engineers, Designers, Lawyers, Business experts and more. But most importantly we wake up every day, and proudly call ourselves as problem solvers. Yet, there’s one problem for which we seem to come up with the weakest of solutions. Much has been spoken about it, and still so much to be done. Let’s talk about the Women in Tech.
JSON extraction made simple
While JSON seems to have taken over the world, there are glaring problems for both developers and non-techies alike — attempting to parse and manage the data.
The Biotech data dilemma and the Mammoth solution
The Biotechnology, Bioinformatics and Computational Biology fields are growing on multiple fronts, including data volume and complexity. The amount of data generated is far larger than the ability to consume or make sense of it. While data volume may sound like a problem, there is another more significant and pressing issue, specifically around existing processes and general accessibility to data.
Combine two tables — by time series — without coding
Here’s a scenario (that happens to be typical in the manufacturing world) – Dataset A (ERP data) contains a date range, and Dataset B (Sensor Data) contains rows that fall within the date range of dataset A. Example: The Objective Our goal is to combine the two datasets with “Time” as the common column. We […]
Hiring additional data engineers is a problem, not a solution
While the tendency to throw in more data scientists and engineers at the problem may make sense if companies have the budget for it, that approach will potentially worsen the problem. Why? Because the more the engineers, the more layers of inefficiency between you and your data. Instead, a greater effort should be redirected toward empowering knowledge workers / data owners.
Announcing our partnership with NielsenIQ
We’re really pleased to have joined the NielsenIQ Connect Partner Network, the largest open ecosystem of tech-driven solution providers for retailers and manufacturers in the fast-moving consumer goods (FMCG/CPG) industry. This new relationship will allow FMCG/CPG companies to harness the power of Mammoth to align disparate datasets to their NielsenIQ data.
Mammoth Analytics achieves SOC 2, HIPAA, and GDPR certifications
Mammoth Analytics is pleased to announce the successful completion and independent audits relating to SOC 2 (Type 2), HIPAA, and GDPR certifications. Going beyond industry standards of compliance is a strong statement that at Mammoth, data security and privacy impact everything we do. The many months of rigorous testing and training have paid off.