Your business needs to better use its data — but what does that mean? Context matters. Data governance, reporting and analytics, business intelligence. When you approach your data architecture, first start with asking the right questions that solve business challenges. What data does the sales team need to increase sales by x %? What data does the engineering team need to work on and innovate products that provide competitive advantage?
Similar questions have inspired companies to disrupt markets. Uber started with asking questions like, how can we optimize drivers at the right locations with the most customer demand? How can we give consumers the ability to call a cab with a click of a button? Tomasz Tunguz highlights similar examples in his book Winning with Data, where he states that “the best data-driven companies operationalize data.” To operationalize data is where companies can use the right data to rapidly change the way they operate.
In order to use the right data, ask the right questions. Gartner states exactly this, “Ask the right questions” in the 2015 article Big Data Analytics Failures and How to Prevent Them. Simply, what problem or toughest business challenge is your company trying to solve with data?
At the foundation and beginning practice of enterprise architecture, dating back to the 70’s with the Zachman Framework, the principle question related to data was “what data is needed to list the things most important to the business?” The framework focuses on the “what”, what is needed, in order to produce the right enterprise, data and technology models for the best use of data.
In order to harness the “Power of Data”, HBR suggests that companies start with the business problem in mind, and then “seek to gain insights from vast amounts of data.” With stating the specific business problem, companies can narrow the search and refine how they are going to find data-driven answers to their most challenging business problems.
With a finite budget, resources, and capacity for change, your enterprise is challenged with how to effectively manage budgets. Prioritization within the enterprise portfolio is key to optimizing the right spend for the right initiatives. Using key prioritization data for the portfolio helps with determining what to focus on.
Continue reading “Enterprise investment management simplified: Budget efficiencies”
To build on investing in the right things, at the right time, the enterprise should prioritize top strategic initiatives within the enterprise portfolio. Using data to inform prioritization decisions is key. Executives and senior leaders want top initiatives to align to strategy, objectives, measurable business outcomes and financial factors (defined in the business case, which was covered in my previous post).
A Prioritization Scorecard calculates priority data that is needed to inform decisions, which are strategic drivers, or key values that align to strategic goals, and detractor information, key values that take away from the project. The data attributes for the key values should be taken directly from the business case. The key outcome is using this scorecard data to generate collective buy-in from senior leadership on top strategic priorities.
Continue reading “Enterprise investment management simplified: Prioritization”
How can you help your department invest in the right things, at the right time? The way you develop your plan can help clarify what your priorities and goals are — and having clear goals is an important part of being able to achieve those goals.
Start with the basics. The foundation is a business case. You want your company’s senior executives, to all levels of employees, to clearly understand what you are trying to achieve with an initiative for a product or service offering.
Continue reading “Enterprise investment management simplified: The business case”
JBoss Enterprise Application Server 7 has been out since June, and if you build and deliver using a Java EE environment and haven’t yet upgraded to EAP7, it’s time to make the jump.
Here’s a look at what’s new in JBoss EAP 7, what has changed since JBoss EAP 6, and how to get the most out of JBoss EAP 7 as your Java EE7 server.
JBoss EAP 7 is based on WildFly Application Server 10, which provides a complete implementation of the Java EE 7 Full and Web Profile standards. WildFly 10 does much to simplify modern application delivery based on containers and microservices.
JBoss EAP 7 features certified support for Java EE7 and Java 8 SE. The WildFly integration brings experimental Java 9 support, too. It also supports current development snapshots of Java 9, which is expected for release this fall.
The JBoss EAP 7 release is available for download from JBoss.org.
Continue reading “Five features of JBoss EAP that will help get you production ready”
We are excited to announce the release of Red Hat JBoss Data Virtualization 6.3.
Data integration has always been challenging. Modern technology trends like big data and cloud, coupled with the need for more real time data access and analysis, are adding to the complexity facing enterprises today.
JBoss Data Virtualization is a data access and integration solution that offers an alternative to physical data consolidation and data delivery by allowing flexibility and agility in data access. Data virtualization creates a single logical view of data from varied data sources, including transactional systems, relational databases, cloud data stores, and big data stores.
What’s New in JDV 6.3
JBoss Data Virtualization 6.3 adds capabilities to help organizations integrate big data and provide high performance data access in real time. The release notes cover the full features and enhancements for JBoss Data Virtualization 6.3. There are a number of new features to enable expanded connectivity, enhanced security and developer productivity support.
Continue reading “Announcing Red Hat JBoss Data Virtualization 6.3”
Database Trends and Applications (DBTA) announced its data solutions winners earlier this August, and one of our middleware products was honored! Red Hat JBoss Data Virtualization won best data virtualization solution.
DBTA’s awards were reader’s choice, meaning that it was the community of data virtualization users who voted for JBoss Data Virtualization. According to DBTA’s announcements, the hallmarks of a winning data virtualization solution include three characteristics:
- Agile development
- A secure virtual data layer
- Real-time data access and provisioning
It’s a combination of security and speed.
Data virtualization provides a layer over existing, separate data sources, which integrates the data in those sources without have to manually copy or convert that data. Data virtualization can support a lot of potential business benefits, including reducing duplicate data, improving data consistency, and reducing architectural complexity. Data virtualizations can provide that comprehensive view and access to data, without having to replace existing applications.
Find out more about Red Hat JBoss Data Virtualization here
What if you could take the streams of information coming into your business and use it to recognize potential opportunities or issues almost immediately? Fabio Marinelli (senior architect) and Syed Rasheed (product marketing manager) will be conducting a webinar on complex event processing. Complex event processing helps you recognize important patterns within your data streams in near real-time.
There are two parts to understanding complex event processing. First is looking at the data itself, from a variety of sources (such as social media, devices, web or mobile applications, monitoring applications). Being able to take different types of information from unrelated sources and get a holistic view is important. The second part is designing an architectural framework that supports that level of data and processing. This webinar looks at an in-memory data grid as a complex event processing engine and using a distributed architecture for dynamic scalability.
Registration is open. The webinar is August 23 at 11:00am Eastern Time (US).
Fun Follow Up: Webinar Q&A
I will collect any questions asked during the webinar, and I’ll do a follow-up post on Friday, June 24, to try to capture the most interesting questions that arise.
Gartner has a term for information which is routinely gathered, but not really used: dark data. This is information which is collected for a direct purpose (like processing an online transaction), but then never really used for anything else. By IDC estimates, dark data represent about 90% of the data collected and stored by organizations.
The Internet of Things (specifically) and digital transformation (more generally) are business initiatives that try to harness that dark data by incorporating new or previously untapped data streams into larger business processes.
Big data refers to that new influx of data. The “big” adjective can be a bit misleading — it doesn’t necessarily mean that these are massive amounts of data. Some organizations may be dealing with petabytes of data, but some may only be gigabytes. It’s not a given amount of data, but rather the scale of increase from previous data streams.
Continue reading “What Are You Getting from (Big) Data?”