Data Quality in Financial Institutions

Corinium

Datacon Africa DataCon Africa Insights Data and AnalyticsIn the last decade regulatory requirements in financial services increased significantly.

Why Data Quality Matters

The Data Administration Newsletter

Quality is never an accident. ” John Ruskin, prominent Victorian era social thinker Data-driven decision-making is fast becoming a critical business strategy for organizations in every sector. It is always the result of intelligent effort.”

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Video: Why Businesses Struggle with Data Quality

Corinium

Matthew Bernath, Head of Data Analytics at Rand Merchant Bank, discusses why ensuring data is high quality remains a key challenge for businesses today with Corinium's Craig Steward. Data and Analytics

Data Quality — You’re Measuring It Wrong

DataFloq

This article was first posted on Towards Data Science.One of our customers recently posed this question:“I would like to set up an OKR for ourselves [the data team] around data availability. Big Data Strategy

Why B2B Contact and Account Data Management Is Critical to Your ROI

64% of successful data-driven marketers say improving data quality is the most challenging obstacle to achieving success. Given data’s direct impact on marketing campaigns, reporting, and sales follow-up, maintaining an accurate and consistent database is a top priority for B2B organizations. This latest eBook aims to help marketing leaders understand the impact of data management on their company’s ROI.

Expanding Role of Data Governance, Metadata Management, and Data Quality

The Data Administration Newsletter

Ensuring data quality is an important aspect of data management and these days, DBAs are increasingly being called upon to deal with the quality of the data in their database systems more than ever before. The importance of quality data cannot be overstated.

3 Points To Place Checks For Data-Quality

DataFloq

Data is the most valuable asset in the digital economy. Be it nurturing existing clients or expanding into new territories, data is the foundation on which new initiatives are built and execution is reviewed against. Data Ingestion. Big Data Technical

Data Quality vs Data Condition: The Power of Context

Anmut

This is true for life in general, but it’s especially applicable to the data you use to power your business. Data without context is just meaningless noise, and any effort to improve or extract value from your data without considering the larger business context is doomed to fall short.?

Solving the Insurance Industry’s Data Quality Problem

Corinium

Using data to inform business decisions only works when the data is correct. Unfortunately for the insurance industry’s data leaders, many data sources are riddled with inaccuracies. Data is the lifeblood of the insurance industry. Data and Analytics

How to Fix Your Data Quality Problem

DataFloq

Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Not surprisingly, one of the most common questions customers ask me is “what data tools do you recommend?”. Big Data Strategy

Dont Acquire a Company Until You Evaluate its Data Quality

DataFloq

The companies join workforces, systems, infrastructure, and data to become a new, more powerful, more valuable, more effective entity. That is only until they realized they overlooked or underestimated the key issues with data, IT infrastructure & integration plans.

How to Overcome the Pain Points of Your CRM

CRM software is a powerful tool when used correctly, yet another obstacle to a sales team’s efficiency when it’s not. Leveraging research and commentary from industry analysts, this eBook explores how your sales team can get back valuable time by overcoming some pain points with your CRM, such as low adoption rates, integrations, and data quality.

Improving Data Quality for the Insurance Industry – Why and How

DataFloq

we all know, insurance is a customer centric industry and highly dependent on data. It would be fair to call data the foundation for the industry. Let’s find out how data quality can make or break your insurance business. Big Data

Controlling Data Quality: Tips and Tools

Dataiku

Only 8% of CDOs are content with the quality of data at their disposal. Data needs to be valuable, thus of high quality , to drive machine learning model success.

Data quality assurance in BI - based on CRM data

ScienceSoft

No more wrong data analysis caused by bad data! Be armed with our new blog post on data quality assurance in the context of CRM

Are you struggling to get started with Data Quality?   Interview with IQ International

Corinium

We are pleased to be working with our media partner, IQ International on our Chief Data & Analytics Officer Brisbane event, where they will be sharing some of their work in developing best practice data quality metrics for every industry. CDAO Sydney Data and Analytics

Data Quality: Making Change a Choice

DataFloq

Big DataIn the modern world, nothing stays the same for long. We live in a state of constant change; new technologies, new trends and new risks. Yet it’s a commonly held belief that people don’t like change. Which led me to wonder, why do we persist in calling change management initiatives “change management” if people don’t like change. In my experience, I have not found this maxim to be true.

Best Practices for a Marketing Database Cleanse

As frustrating as contact and account data management can be, this is still your database – a massive asset to your organization, even if it is rife with holes and inaccurate information. Entrusting a vendor to help maintain its accuracy and completeness is no ordinary engagement. Download ZoomInfo’s latest data-driven eBook aimed to help marketing leaders understand the best practices around choosing a B2B contact data provider.

Why No One Cares about Poor Data Quality

Jim Harris

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris. Why does no one care about poor data quality? tolerance for poor data quality). Data Quality Podcasts OCDQ Radio

Your Guide to Data Quality Management

ScienceSoft

Setting up data quality management seems to be a blurry task? These best practices will help you improve the quality of your data and, ultimately, your decisions We show what a well-organized process looks like and enumerate the required tools.

Using Extensions to Increase Tableau Data Quality

Tamr

This post is the first in a series of blogs about Tableau and our Tableau extension for data issue tracking, Tamr Steward. Tableau extensions increase functionality in Tableau, the versatile tool for data analytics and visualization. Outside extensions open new doors to increase Tableau data quality and build better dashboards. Writebacks, analytic tools to measure user engagement, and self-service tools all help increase Tableau data quality, improving dashboards.

Tips to Create Effective Data Quality Rules

TDAN

You know you can rule the quality of your data. Read on to learn simple but effective tips on how to make superb data quality possible. The bad news is: Low-quality data costs companies about $15 million per year, according to Gartner’s Data Quality Market Survey. So why don’t you? The good news is: Your […].

How to Overcome the Pain Points of Your CRM

When used effectively, a CRM can be the life blood of your sales team – keeping everyone organized, efficient, and at peak productivity. However, as a company, sales stack, and database grow, it becomes difficult to uphold structure and governance to keep a CRM up-to-date. The result? Less organization, more confusion, and fewer deals closed. Leveraging leading industry research from industry analysts, this eBook explores how your sales team can gain back valuable time.

The Gold Standard – The Key to Information Extraction and Data Quality Control

Ontotext

In much the same way, in the context of Artificial Intelligence AI systems, the Gold Standard refers to a set of data that has been manually prepared or verified and that represents “the objective truth” as closely as possible. How does the Gold Standard help data linking?

Stop your Data Quality Project and Start your Outcome-based Data Governance Program

Andrew White

I spoke to a client recently who had this question: “I need help to develop a survey to ask our employees around the company how they regard the various subjective metrics tied to data quality.” What was the point of the data quality work?

Data Quality and Chicken Little Syndrome

Jim Harris

The sales pitches for data quality solutions often suffer from Chicken Little Syndrome, when vendors and consultants, instead of trying to sell the business benefits of data quality , focus too much on the negative aspects of not investing in data quality , and try scaring people into prioritizing data quality initiatives by exclaiming “your company is failing because your data quality is bad!” Data Quality Debates

Balance Data Quality with Data Agility!

Smarten

Data Quality vs. Data Agility – A Balanced Approach! When it comes to analytical quality versus analytical agility, we might see the issue in the same light. Sometimes we are so focused on perfection that we do not see the benefit of agility.

Forrester Research Report: How Sales and Marketing Intelligence Drive Improved Business Outcomes

In 2019, DiscoverOrg commissioned Forrester Consulting to evaluate sales and marketing intelligence practices in the B2B space. The primary takeaway? Forrester found only 1.2% of companies achieved a score indicating maturity in data management practices. However, organizations are fighting back - and winning.

Data Quality Assessment Is Not All Roses. What Challenges Should You Be Aware Of?

KDnuggets

Of all data quality characteristics, we consider consistency and accuracy to be the most difficult ones to measure. 2019 Sep Opinions Challenges Data QualityHere, we describe the challenges that you may encounter and the ways to overcome them.

Improving Data Quality With an Efficient Data Labeling Process

Dataiku

A key principle behind data preparation that data scientists regularly hammer home is that of garbage in, garbage out — if your data is flawed going into a machine learning process, you are bound to receive flawed results, algorithms, and worse, business decisions.

Finding Data Quality

Jim Harris

Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you? This blog post is an hommage to not only the film, but also to the critically important role into which data quality is cast within all of your enterprise information initiatives, including business intelligence, master data management, and data governance. Data Silos. Data Profiling. “I Data Qualit

Data Quality in Six Verbs

Jim Harris

Once upon a time when asked on Twitter to identify a list of critical topics for data quality practitioners, my pithy (with only 140 characters in a tweet, pithy is as good as it gets) response was, and especially since I prefer emphasizing the need to take action, to propose six critical verbs: Investigate , Communicate , Collaborate , Remediate , Inebriate , and Reiterate. 1 — Investigate Data quality is not exactly a riddle wrapped in a mystery inside an enigma.

A Guide to Better Data Quality

Without high quality data that we can rely on, we cannot trust our data or launch powerful projects like personalization. In this white paper by Snowplow, you'll learn how to identify data quality problems and discover techniques for capturing complete, accurate data.

Data quality: The key to building a modern and cost-effective data warehouse

IBM Big Data Hub

Turning raw data into improved business performance is a multilayered problem, but it doesn’t have to be complicated. To make things simpler, let’s start at the end and work backwards. Ultimately, the goal is to make better decisions during the execution of a business process.

Data Conversations Over Coffee with Matthew Bernath

Corinium

Data & Coffee - Two Things That Demand The Highest Quality. Data and Analytics Data Quality Training Online

How in-line address data quality delivers business ready data for AI initiatives

IBM Big Data Hub

Imagine opening your mailbox and seeing a letter addressed to “current resident,” or having your financial institution’s AI powered digital assistant inform you that your replacement card is on its way to your old address

How DataOps enabled Standard Bank to gain data quality and agility during changing market conditions

IBM Big Data Hub

Follow @IBMAnalytics. DataOps journey

The Alation State of Data Culture Report - Q1 2021

Companies are expected to spend nearly $23 billion annually on AI by 2024. What could go wrong? This report explores AI obstacles, like inherent bias and data quality issues, and posits solutions by building a data culture.

Video: Data Conversations Over Coffee with Maciej Kaliszka (DQT)

Corinium

In episode 6 of Data Conversations Over Coffee we talk to Maciej Kaliszka about building a fully accredited Data University! Data and Analytics Data Quality Training Online

Video: Data Conversations Over Coffee with Caroline Carruthers (DQT)

Corinium

In episode 5 we talk to the co-author of two data focused books - Caroline Carruthers of Carruthers & Jackson. Data Quality Training Online

Video: Data Conversations Over Coffee with Ben Jones (DQT)

Corinium

This is episode 3 of Data Conversations Over Coffee and we're speaking to Ben Jones. Data and Analytics Data Quality Training Online

Video: Data Conversations Over Coffee with Alex MacPhail

Corinium

In episode 7 of Data Conversations Over Coffee we talk to Alex MacPhail who is a pilot and keynote speaker on leading high performing teams. Data and Analytics Data Quality Training Online DataCon Africa Live

Data Management on Display at Informatica World 2019

David Menninger's Analyst Perspectives

Under that focus, Informatica's conference emphasized capabilities across six areas (all strong areas for Informatica): data integration, data management, data quality & governance, Master Data Management (MDM), data cataloging, and data security. Big Data Data Quality Master Data Management Data Governance Data Management Informatica data lakes Informatica World Data Storage