Tag: Data Preparation

Inside the Mind and Methodology of a Data Scientist

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn’t help reduce the confusion when every tech vendor rebrands their products as AI.

Read More


How “Able” Is Your ETL Process? 8 Ways To Modernize Data Prep, Part 2

In Part 1 of this blog, I gave a high-level comparison of traditional extract, transfer and load (ETL) tools, desktop data preparation tools and Birst’s modern, built-for-the-cloud ETL tools for data analytics. In this blog, I’ll dive deeper into the eight key ways that, of the three options, Birst is best “able” to meet the rigorous requirements of today’s enterprise users.

Read More


How “Able” Is Your ETL Process? Modernizing Data Prep, Part 1

For all the exciting discovery that data analytics enables, data preparation involves, for most users, an equal amount of drudgery. That’s true for a number of reasons, first and foremost being that enterprise data is rarely structured for analytic use; it’s often designed for transactional system performance or to minimize storage. Wrangling in data that’s spread across different locations and technologies (database, cube, cloud-based, on-premises, flat files, etc.), and then cleaning up “dirty” (incorrect, improperly encoded, duplicated or blank) data is a time-consuming and labor-intensive task, constantly repeated as data sources come and go.

Read More


What AI Means to a Data Scientist

How many times have you wished for more hours in the day so you can complete more tasks? A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. However, there are simply not enough data scientists in the world to deliver on the AI potential. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases?

Read More


Will AI Increase the Reach of BI and Analytics?

The business intelligence (BI) assembly line is broken, with adoption or utilization rates of only 30 percent in a typical organization, according to Gartner[1]. These adoption rates include all the users of the BI system – administrators who manage the system, analysts who build reports, and business users who consume reports for better decision making. This underutilization ignores employees outside the BI system who could be using valuable data to make better decisions. No one can argue that business performance and innovation are negatively impacted when data assets are not fully leveraged for decision making.

Read More


Improve your company’s bottom line with analytics

The explosion of data in the financial services industry is creating new opportunities for business intelligence and analytics. At the same time, this explosion presents significant challenges. Over 2.7 zettabytes of data exist in the digital universe today. Every day, financial services companies seek to use these massive volumes of data to increase margins, reduce risk, boost customer satisfaction, and win in a globally competitive marketplace.

Read More


Cloud Analytics: Your Key to Modern BI Capabilities that Drive Business Success

In The Forrester Wave™: Enterprise BI Platforms With Majority Cloud Deployments, Q3 2017, Forrester Principal Analysts Boris Evelson and Martha Bennett noted that, “The winners in the digital economy will be those that are able to gain insights the fastest and take appropriate action. To do so, companies need a solid data and analytics foundation, and cloud-based BI platforms are an essential part of this foundation.” In fact, among those organizations that Forrester surveyed in 2017, 45% said that they had “either already moved some or all of their BI capabilities to the cloud or were planning to do so.”

Read More


Making Decisions with Data – Is Data Prep Really Preparing You for Success?

article image

Data preparation is an essential tool for everyone who wants to get value from sources of information. Whether this data is held in internal applications, derived from partners or supplied by third parties, it will need some work before it can be analysed. More importantly, prepared data must be connected and related to other analytic instances in the enterprise, so teams can collaborate and make better decisions with data.

Read More


Making Decisions with Data – The Role for Data Preparation

article image

Making use of data within companies is one of those challenges that should deliver huge results. In its FutureScape predictions for 2016, analyst firm IDC estimated that, worldwide, those companies analyzing all their information for actionable insight should see an extra $430 billion in productivity gains, compared to those that did not look at all their data.

Read More