Breaking Through The Data Bottleneck: A Customer Case Study In Agile ETL
As a sequel to our webinar, “Breaking Through Your Data Bottleneck With Agile ETL”, we’ll be interviewing our customer, FairPoint Communications on the challenges faced while making the transition from traditional ETL processes to Lavastorm Agile ETL. Fairpoint will specifically address how this approach accelerated projects without compromising governance, quality and accuracy.
Vendor Landscape – Data Preparation Tools
Analysts and consumers of data both spend significant amounts of time wrangling data in order to conduct analyses and gain insights. The data in their systems lacks the context of their questions, decisions, or business actions. Enterprise architects take note: New big data environments, faster data integration, and analytic appliances aren’t the answer. Your analysts need better tools to speed up data preparation efforts that ultimately lead to faster, deeper insights that matter to the business.
Building Highly Agile, Impactful, and Respected Analytics Centers of Excellence
The Agile Solution for Successful Analytics Centers of Excellence
Most Analytics Centers of Excellence do not deliver results. To address these challenges, Blue Hill Research conducted deep primary research with leading consultancies, systems integrators, and multi-national companies to understand how to construct successful analytics CoEs.
Investing in Agile Analytics Expansion
To achieve data-driven differentiation, organizations must overcome complex data challenges, contending with social, mobile, and IoT-enabled data sources that drive new technology investments. Blue Hill’s James Haight and Hyoun Park investigate the decision-making and operational process of organizations that have succeeded in navigating such environments.
Balancing the Demands for Business Agility with IT Governance and Control
Data discovery is upending the traditional BI approach by empowering users with the capability to combine and analyze data on their own, independently from the IT organization. The need for business agility and self-service analytics is changing the governance equation. Finding the right balance between business agility and IT control or governance is, therefore, essential to having an effective self-service analytic initiative.
Drive Business Insight With Effective BI Strategy
How does BI differ from reporting and management information systems? This road map will give you an understanding of the four critical steps in strategizing around BI to achieve business goals: 1) establish the value of BI; 2) set the right strategy; 3) execute the strategy with precision; and 4) measure and optimize results.
Build An Agile BI Organization
This report provides an organizational framework that describes how application development and delivery professionals working on BI initiatives can align their BI organization for agility; it’s a key part of Forrester’s Agile BI tetrad — Agile BI software development, organizations, processes, and technologies.
Unleash the Power of Tableau
Tableau’s stunning success has empowered individuals across the enterprise to create powerful data visualization and analysis on-demand. However, the challenge lies in the time and effort it takes to prep data for visualization.
Democratizing Data & Predictive Analytics Software
As organizations empower more users to fully leverage advanced and predictive analytics software to “democratize” their data and bring insights to the masses through interactive visual dashboards, they also need to provide enhanced data transparency, collaboration, and governance to gain broader acceptance and trust of the data.
Citizen Data Scientists: How Self-service Tools Can Democratize Data
The tsunami of valuable data has hampered the ability of the few highly skilled technologists to take advantage of it. To bridge the gap, more and more companies are providing advanced analytics tools to its super users – a group Gartner calls “citizen data scientists”
Linking together reactive and proactive data iteration
This firm sought an efficient and transparent visualization process to support their growing data needs and gain a competitive sales and market advantage using agile business analytics for big data.
Transforming the Audit Process
Read this case study to see how PwC uses Lavastorm to dig deeper, broader, and smarter.
Overpowering Data Quality Issues and Massive Data Volume Increase
See how E.ON used Lavastorm to eliminate data gaps, clean up data quality issues, reduce management reports, get more value out of their BI investment, automate data quality improvement efforts, and improve management team understanding of data quality initiatives.
See how a major Asia-Pacific telecommunications company uses Lavastorm to migrate data faster than SQL methods, perform rapid root cause analysis to speed resolution, provide visual assurance of data and process integrity, and process accurate data transformation on a massive scale.
Five Ways to Empower Business Analysts & Succeed in Your Self-Service BI Program
Many BI projects fail when not controlled by the business. Lack of proper requirements gathering and the inability to meet the needs of users creates a lack of adoption. Simple tools such as Excel and Access can no longer handle the complexities and increasing volumes inherent in big data or maintain the validity of analytic models. Read this new Lyndsay Wise paper for a look at five key enablers of self-service BI for business analysts.
Solving Data Integration Challenges with the Lavastorm
Accessing, preparing, and integrating data from different sources is usually the toughest part of any analytics project. Inconsistent formats, missing data, inaccurate data are just some of the problems that you face. Lavastorm wipes away those common data integration problems with a streamlined approach to acquiring and combining just about any type of data – 10x faster than other methods.
Regulatory Reporting for Financial Services
Financial services firms face an increasing regulatory burden. Reports need to be more detailed, comprehensive, and accurate – and delivered more quickly. Compliance is a struggle using spreadsheets and inflexible legacy systems. A new solution is needed. Lavastorm Analytics software lets firms quickly create auditable regulatory reports. Business users can aggregate data, check data quality, analyse data, and generate reports – all without expensive and time consuming IT efforts. Plus, Lavastorm provides the flexibility to handle ad hoc requests and changing requirements while simultaneously strengthening IT governance efforts.
Why Most Big Data Projects Fail
Organizations need to make big data analytics an integral part of their everyday workflows, which means continuously running analytics and automating processes. For this to work seamlessly, organizations need to tear down the walls between IT and business, remove hurdles, to strike a balance between demands for business speed and agility with IT governance and control. Tools such as Lavastorm help organizations capture big data-based insights while enabling a more agile and analytics-driven business culture.
Four Situations Where You Need Agile Data Management
Enterprises awash in data often struggle to answer basic business questions because they can’t supply data to the people who need it, when they need it. Situations where you’re dealing with, exploring external data, changing data sources, adjusting business logic and changing data fields are all desperate for agile data management. Organizing, prepping, transforming, and provisioning useful data shouldn’t be difficult. In fact, it isn’t: if you’re using the right tool. And that’s what agile data management is all about.
Breaking Through the Analytic Limitations of Access and SQL
Although they are often a cornerstone of a company’s analytic toolkit, traditional databases, such as Access, and query tools, such as SQL, are designed for storage and simple queries, not for creating the complex analytics that are required by today’s fast-moving businesses. New technologies can help organizations get over the analytic limitations of Access and SQL- especially when dealing with the demands to processing more data, changing analytics more frequently, and making analytics available to more decision makers.
How to Find Happiness in your Data Warehouse Architecture
When organizations want to improve data quality, organize data for analytics, or develop a “single version of the truth”, they immediately think they need to start with a data warehouse architecture. But when is using a data warehouse architecture the wrong move? When FairPoint Communications wanted to organize data from far-flung parts of the company, they turned off their data warehouse architecture in favor of an agile data management and analytics solution that acts as a virtual data. The alternative approach brought agility to their organization and transparency that benefited decision makers in all departments.