Sisense

OCTOBER 21, 2020

As a member of the data team, your role is complex and multifaceted, but one important way you support your colleagues across the company is by building and maintaining data models. Picking a direction for your data model. Think like a designer. However, just asking your users, “What do you want?”

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Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. Without big data in predictive analytics, these descriptive models can’t offer a competitive advantage or negotiate future outcomes.

Predictive Analytics 304

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Astera

OCTOBER 7, 2022

The Rise of Unstructured Data Analytics. Until recently, enterprises solely relied on structured data to make business decisions — as conventional software couldn’t ingest, process, and extract the information from unstructured text mainly due to… the lack of structure. Why Is Unstructured Data Analytics Important?

Data Analytics 52

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Insight Software

AUGUST 4, 2023

Providing valuable insights from data that moves the business forward in achieving its strategic objectives is one of the most valuable skills any FP&A or Operational Planning (OP) professional can possess. Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.

Data Analytics 52

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Smart Data Collective

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Unfortunately, despite the growing interest in big data careers, many people don’t know how to pursue them properly. Where to Use Data Mining?

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Smart Data Collective

APRIL 14, 2021

Advancement in big data technology has made the world of business even more competitive. The proper use of business intelligence and analytical data is what drives big brands in a competitive market. This high-end data visualization makes data exploration more accessible to end-users.

Business Intelligence 333

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Smart Data Collective

APRIL 2, 2019

These massive storage pools of data are among the most non-traditional methods of data storage around and they came about as companies raced to embrace the trend of Big Data Analytics which was sweeping the world in the early 2010s. Big Data is, well…big.

Big Data 208

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Smart Data Collective

MAY 29, 2023

The Role of an Effective Analyst Data analysts are responsible for the harvesting, management, analysis, and interpretation of big data gathered. Skills Sets to Look For When entering into the hiring process for a data analyst there are a few skills that are recommended to look for when narrowing down the pool of options.

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Analysts Corner

JUNE 18, 2023

Python, Java, C#) Familiarity with data modeling and data warehousing concepts Understanding of data quality and data governance principles Experience with big data platforms and technologies (e.g., Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

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Astera

APRIL 27, 2023

The modern data stack (MDS) is a collection of tools for data integration that enable organizations to collect, process, store and analyze data. Being based on a well-integrated cloud platform, modern data stack offers scalability, efficiency, and proficiency in data handling.

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Sisense

JANUARY 15, 2021

This is one of the reasons we’ve seen the rise of data teams — they’ve grown beyond Silicon Valley startups and are finding homes in Fortune 500 companies. As data has become more massive, the technical skills needed to wrangle it have also increased. Situation #2: Established company creates a data team for deeper insights.

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Astera

MAY 25, 2023

Unlocking the Potential of Amazon Redshift Amazon Redshift is a powerful cloud-based data warehouse that enables quick and efficient processing and analysis of big data. Amazon Redshift can handle large volumes of data without sacrificing performance or scalability. These include dimensional models and data vaults.

Data Warehouse 52

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Cprime

MAY 4, 2023

Key Data Integration Use Cases Let’s focus on the four primary use cases that require various data integration techniques: Data ingestion Data replication Data warehouse automation Big data integration Data Ingestion The data ingestion process involves moving data from a variety of sources to a storage location such as a data warehouse or data lake.

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Astera

NOVEMBER 14, 2023

And consequently, having a constantly evolving architecture means you will have access to accurate, up-to-date data to fuel your analytics, allowing teams and departments to meet their respective goals.

Data Warehouse 52

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The BAWorld

APRIL 11, 2022

There’s never been a better time to broaden your data analytics knowledge. Still, if you’re considering getting a data analytics certification, you’ll want to know if it’s worth it. But which data analytics qualifications are the best? Skills Required to Become a Data Analyst.

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The BAWorld

APRIL 11, 2022

There’s never been a better time to broaden your data analytics knowledge. Still, if you’re considering getting a data analyst certifications, you’ll want to know if it’s worth it. But which data analytics qualifications are the best? Skills Required to Become a Data Analyst.

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Astera

AUGUST 22, 2023

Uncover hidden insights and possibilities with Generative AI capabilities and the new, cutting-edge data analytics and preparation add-ons We’re excited to announce the release of Astera 10.3—the the latest version of our enterprise-grade data management platform. Step Into the Future: Take Charge with Astera 10.3!

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Whizlabs

DECEMBER 23, 2020

However, organizations also face the need for ideal infrastructure for the storage, analysis, and processing of large volumes of data. Apache Cassandra has been one of the prominent names in the field of big data analytics for quite some time. Read Now: How NoSQL is better for big data applications.

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Astera

OCTOBER 27, 2023

Transitioning to a different cloud provider or adopting a multi-cloud strategy becomes complex, as the migration process may involve rewriting queries, adapting data models, and addressing compatibility issues. Dimensional Modeling or Data Vault Modeling? We've got both!

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Sisense

SEPTEMBER 3, 2020

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! In The Future of Work , we explore how companies are transforming to stay competitive as global collaboration becomes vital.

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The BAWorld

SEPTEMBER 2, 2021

This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. Unstructured data has no definite structure or data model and is stored in its native format. of organizations are investing in big data. As per New Vantage, 97.2% Did You Know?

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Sisense

JUNE 12, 2019

Living in a World of Big Data. It all starts with the data. More than a decade ago, when the term Big Data emerged, companies started to invest heavily in the infrastructure to gather and store their data, realizing the potential future value of their data. Building a Better Tomorrow.

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Sisense

JULY 24, 2019

Last, and still a very painful challenge for most users, is the familiarity with the underlying data and data model. NLQ is gaining traction in the big data analytics tools domain for its quick answers and ease of use. In other words, how the variables are named, and the granularity of their values.

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Marutitech

DECEMBER 17, 2021

You must be wondering what the different predictive models are? What is predictive data modeling? Which predictive analytics algorithms are most helpful for them? This blog will help you answer these questions and understand the predictive analytics models and algorithms in detail.

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Actian

MAY 21, 2019

The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.

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Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. By understanding the power of ETL, organisations can harness the potential of their data and gain valuable insights that drive informed choices.

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Data Pine

MARCH 31, 2022

A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis. They can help a company forecast demand, or anticipate fraud.

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Smart Data Collective

JUNE 16, 2022

A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on. Types: HOLAP stands for Hybrid Online Analytical Processing. This provides both the ROLAP model’s data efficiency and the MOLAP model’s performance.

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Whizlabs

APRIL 19, 2021

Michelle has more than 20 years of experience in the field of research in statistics, data analytics, consulting and market research. As a Chief Customer Officer, she is expert in cloud-based subscription models, automation and data analytics to drive customer adoption of software and reduce churn.

Cloud Computing 145

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Insight Software

MAY 1, 2023

Third-party data might include industry benchmarks, data feeds (such as weather and social media), and/or anonymized customer data. Four Approaches to Data Analytics The world of data analytics is constantly and quickly changing. Ideally, your primary data source should belong in this group.

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