The Role Of Data Warehousing In Your Business Intelligence Architecture

datapine

Effective decision-making processes in business are dependent upon high-quality information. What Is Data Warehousing And Business Intelligence? BI Architecture Framework In Modern Business. Now we approach the data warehousing and business intelligence concepts.

What Role Does Data Mining Play for Business Intelligence?

Jet Global

In the modern era, businesses are continually looking for a competitive advantage—something that will allow them to deliver goods or services at a lower cost, higher quality, and faster speed than their competitors. Data Mining and Business Intelligence.

Business Glossaries and Metadata: Data Governance May Require a Village

TDAN

Is adoption by the business an issue for you? Data Governance occurs best when done in conjunction with the business processes and not as a “bolt on”/additional activity. Is your organization struggling to succeed with your Data Governance program?

Deep Learning Can Make a Difference

TDAN

Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities.

The Non-Invasive Data Governance Framework – The Details

TDAN

The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections.

Data Governance Roles and Responsibilities

TDAN

Roles and responsibilities are the backbone of a successful information or data governance program.

Data Management 20/20: Business Glossary Best Practices

TDAN

Suppose you need to make some business decisions based on the number of […]. It’s all about communication. Everyone talks about collecting, storing, and analyzing data but how do you make use of this data if you cannot understand it?

All in the Data: CDOs Should Be Asking “How” … and Not “Why”

TDAN

The secret lies with Data Governance. The Chief Data Officer (or whoever the Data Czar is at your organization) needs to get past, and I mean way past, the “Why is Data Governance important?” or “Why do we need Data Governance?” questions if they are ever going to be successful czar-ing the data. Rather, the […].

All in the Data: Calm Management’s Fears About Data Governance

TDAN

Wouldn’t it be great if you could simply put structure around how your organization governs your data without throwing a lot of money and resources at the problem? The truth is you can. It’s all in the data.

Data is Risky Business: Call for Compulsory Ethics

TDAN

In our book, Ethical Data and Information Management, Katherine O’Keefe and I look at the relationship between the Ethic of Society, which today finds expression this morning, in a report from a UK Parliamentary Committee setting out their findings against Facebook and Cambridge Analytica.

Data Dictionary vs. Business Glossary

TDAN

Let’s take “data dictionary” and “business glossary” for […]. Enterprises today are focused on ensuring robust data governance, and are exploring different tools and approaches to support their efforts.

Measure Twice, Cut Once: How the Right Data Modeling Tool Drives Business Value

erwin

In today’s hyper-competitive, data-driven business landscape , organizations are awash with data and the applications, databases and schema required to manage it. Improve business processes for operational efficiency and compliance.

To Own or Not to Own Data

TDAN

To own data or not to own data, that is the question. This question comes up often when I am speaking with clients or groups of people during my Data Governance webinars and conference presentations.

Common Data Modeling Mistakes and Their Impact

TDAN

Although data modeling has been around for over 30 years, it ranks among the top areas from which database application problems arise. Moreover, the severity of the problems ranges from totally incorrect functionality to freakishly miserable performance.

The Non-Invasive Data Governance Framework – The Framework Structure

TDAN

The following paper is the first of a three-part series that describes the Non-Invasive Data Governance Framework. The framework was developed and is implemented by Robert S. Seiner of KIK Consulting & Educational Services (KIKconsulting.com) and The Data Administration Newsletter (TDAN.com).

Big Data Influence on Restaurants and Catering

TDAN

The restaurant and catering sector is one of the largest industries which serves the expectations of millions daily. While everyone visits establishments like restaurants with their own sets of expectations, it is up to the players in this sector to ensure those expectations are being met.

Five Benefits of an Automation Framework for Data Governance

erwin

Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use. Governing metadata. Supports a wide spectrum of business needs.

The Non-Invasive Data Governance Framework – The Levels and Components

TDAN

Part one of this series addressed the structure of the Non-Invasive Data Governance Framework. In part two, I detail each of the labels on the rows and columns of the framework. I refer to the row labels as the Levels or perspectives of the organization and the column labels as the Core Components of a […].

Trends in Data Management and Analytics

TDAN

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.

DAMA International Community Corner: Announcements & New Chapters

TDAN

Welcome to DAMA Corner, a source of information for data management professionals here in TDAN.com, an industry-leading publication for people interested in learning about data administration, data management disciplines, and best practices. Each column provides an update on the professional organization DAMA International, and an opportunity to share your experience with other professionals that are passionate about data! […].

The Rule of Least Power in Data Analytics – Part 2

Kirk Borne

In this part, you’ll learn about four examples that are highly relevant to today’s data-driven business goals. PERSPECTIVES big data analysis business intelligence Data Minds Kirk Borne

ROI 87

Data Dictionary vs. Business Glossary: The Low Down

Octopai

Let’s take “data dictionary” and “business glossary” for example. It is important to note when differentiating between the two that they are mainly silo – or project-based, and therefore the value they provide across the multi-system business intelligence infrastructure is limited.

Convergent Evolution

Peter James Thomas

Once the output of Data Science began to be used to support business decisions, a need arose to consider how it could be audited and both data privacy and information security considerations also came to the fore. This required additional investments in metadata.

Power BI + Azure Data Lake = Velocity & Scale to your Analytics

Perficient Data & Analytics

The biggest challenge Business Analysts and BI developers have is the need to ingest and process medium to large data sets on a regular basis. The Common Data Model (CDM) provides a shared data language for business and analytical applications to use.

Power BI + Azure Data Lake = Velocity & Scale to Your Analytics

Perficient Data & Analytics

The biggest challenge Business Analysts and BI developers have is the need to ingest and process medium to large data sets on a regular basis. The Common Data Model (CDM) provides a shared data language for business and analytical applications to use.

What is going on in the world of data and analytics?

Andrew White

The notable ones include (in no particular order of importance: The time of business information architecture is now. to weave together the governance and management of master data, application data, and less-widely shared data, and just enough enterprise metadata management. Don’t You Need to Understand Your Business Information Architecture? Metadata and its management across an organization is becoming critical (and this is not the same as ‘metadata management’).

Altus Data Warehouse

Cloudera

Cloudera’s modern data warehouse runs wherever it makes the most sense for your business – on-premises, public cloud, hybrid cloud, or even multi-cloud. Businesses need to be agile and quick in using data to drive insights, lest they lose the opportunity they are trying to capitalize on.

How companies are building sustainable AI and ML initiatives

O'Reilly on Data

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption?

Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Here are some typical ways organizations begin using machine learning: Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application. Metadata and artifacts needed for audits.

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. The query graph provides metadata that gets leveraged for optimizations at multiple layers of the relational database stack. is to externalize graph-based metadata.

Delivering Better Dashboards with Analytic Quality Assurance

Tamr

Companies that recognize this, and establish analytic quality assurance processes that close the loop between dashboard users, analytic developers, and source owners, are far more likely to drive analytic adoption & business outcomes. Great analytic dashboards require great data.

Why You Need a Data Catalog & How to Choose One

Octopai

If the point of Business Intelligence (BI) data governance is to leverage your datasets to support information transparency and decision-making, then it’s fair to say that the data catalog is key for your BI strategy. But to reap the full benefits of the platform, you need to know what the system brings to the table and how to select one that suits your specific business needs. Folding In Metadata Automation.

Data Lakes: What Are They and Who Needs Them?

Jet Global

From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive. Data lakes represent a new frontier for businesses.

5 Advantages of Datorama for Marketers

Perficient Data & Analytics

Business Intelligence (BI). This method works great if your business runs entirely on e-commerce. Here’s why this platform is the leading marketing intelligence solution in the marketplace: Connect and unify ALL of your marketing data.

Modernize Using The BI & Analytics Magic Quadrant

Rita Sallam

There are pivot moments in life and in business where you just know – everything is about to change. Like when Oracle acquired Hyperion in March of 2007, which set of a series of acquisitions –SAP of Business Objects October, 2007 and then IBM of Cognos in November, 2007.

What is Big Data Analytics?

Mixpanel on Data

Big data analysis helps companies manage unusually large, complex, and fast-changing datasets that conventional business intelligence (BI) tools can’t handle. Advances in the field of supercomputing have trickled into the business world.

Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. For definitions, let’s start with a standard textbook/encyclopedia definition: “Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise. ”. Allows metadata repositories to share and exchange.

Building a Self-Managed Shared Data Experience

Cloudera

Worse, the metadata and context associated with that data may be lost forever if a transient cluster is shut down and the resources released. Self-service access to a universal data in a single data store for all of your applications, not siloed into a fragmented service for each type of data science, business intelligence (BI), data engineering, or real-time operational analytics you want to do.

In-depth with CDO Christopher Bannocks

Peter James Thomas

I’m very glad that he has been able to find time in his busy calendar to speak to us. On a day to day basis, we are aligned with the business units and the functional units so we have CDOs in all of these areas. Part of the In-depth series of interviews.

Data Science, Past & Future

Domino Data Lab

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.