David Menninger's Analyst Perspectives

What Makes a Metric a KPI?

Posted by David Menninger on Nov 9, 2021 3:00:00 AM

How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but why leave this evaluation to chance? Why not be more explicit about what results are expected?

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Topics: business intelligence, Analytics, Internet of Things, Data, Digital Technology, Streaming Analytics, AI & Machine Learning

Using Event Data in Financial Services to Improve Business Processes

Posted by David Menninger on Nov 3, 2021 3:00:00 AM

Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and services or revenue.

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Topics: Analytics, Internet of Things, Data, Digital Technology, Streaming Analytics

Databricks Lakehouse Platform Streamlines Big Data Processing

Posted by David Menninger on Oct 26, 2021 3:00:00 AM

Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.

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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning

Use External Data Platform to Improve Analytics

Posted by David Menninger on Oct 19, 2021 3:00:00 AM

Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.

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Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Data, Streaming Analytics

Data Virtualization Brings Data Together Quickly and Easily

Posted by David Menninger on Oct 7, 2021 3:00:00 AM

The technology industry throws around a lot of similar terms with different meanings as well as entirely different terms with similar meanings. In this post, I don’t want to debate the meanings and origins of different terms; rather, I’d like to highlight a technology weapon that you should have in your data management arsenal. We currently refer to this technology as data virtualization. Other similar terms you may have heard include data fabric, data mesh and [data] federation. I’ll briefly discuss these terms and how I see them being used, but ultimately, I’d like to share with you some research that shows why data virtualization can be valuable, regardless of what you call it.

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Topics: Analytics, Data Governance, Data Integration, Data, Digital Technology, data lakes

Alteryx Tackles Analytics Ops

Posted by David Menninger on Oct 5, 2021 3:00:00 AM

Alteryx is a data analytics software company that offers data preparation and analytics tools to simplify and automate data wrangling, data cleaning and modeling processes, enabling line-of-business personnel to quickly access, manipulate, analyze and output data. The platform features tools to run a variety of analytic functions such as diagnostic, predictive, prescriptive and geospatial analytics in a unified platform, and can connect to various data warehouses, cloud applications, spreadsheets and other sources.

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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, AI & Machine Learning

The Weak Link in Data Governance? Analytics

Posted by David Menninger on Sep 30, 2021 3:00:00 AM

Data governance is a hot topic these days. In fact, we are conducting benchmark research on the subject here. With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations face more external oversight of their data governance practices. The risk of significant fines associated with these and other regulations, coupled with organizations’ internal compliance requirements, has brought more attention to data governance practices. We anticipate through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data.

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Topics: Analytics, Data Governance, Data, Digital Technology

Collibra Brings Effective Data Governance to Line-of-Business

Posted by David Menninger on Sep 28, 2021 3:00:00 AM

Collibra is a data governance software company that offers tools for metadata management and data cataloging. The software enables organizations to find data quickly, identify its source and assure its integrity. Line-of-business workers can use it to create, review and update the organization's policies on different data assets. Collibra’s software uses a microservice architecture and open application programming interfaces to connect to various data ecosystems. Its data intelligence cloud platform can automatically classify data from various sources such as online transaction processing databases, master repositories and Excel files without moving the data, so the information assets stay protected.

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Topics: business intelligence, Analytics, Data Governance, Data Preparation, Information Management, Data, data lakes, AI & Machine Learning

Sisu Optimizes Analytics with Machine Learning for Actions & Decisions

Posted by David Menninger on Sep 23, 2021 3:00:00 AM

Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.

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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data, AI and Machine Learning

Rapidminer Platform Supports Entire Data Science Lifecycle

Posted by David Menninger on Sep 16, 2021 3:00:00 AM

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization. Rapidminer Studio is its visual workflow designer for the creation of predictive models. It offers more than 1,500 algorithms and functions in their library, along with templates, for common use cases including customer churn, predictive maintenance and fraud detection. It has a drag and drop visual interface and can connect to databases, enterprise data warehouses, data lakes, cloud storage, business applications and social media. The platform also supports push-down processing for data prep and ETL inside databases to minimize data movement and optimize performance.

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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, data lakes, AI & Machine Learning