Your organization is executing campaigns targeting the millennial generation and depending on social data for deeper customer insights. Managers are asking for more data from multiple sources to support new investments and product strategies. Marketing promotions are adjusted in real time based on current sales data across all channels.
Effectively using and managing information has become critical to driving growth in areas such as pursuing new business opportunities, attracting and retaining customers, and streamlining operations. In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before.
Reason 1: Lower total cost of ownership
Today, it is more important than ever that your IT team use its budget and staff efficiently. You need database software that provides industry leading data management capabilities cost effectively, while reducing the amount of staffing resources needed to meet service-level agreements (SLAs). But to cut management costs, you also need solutions that help automate a range of administrative tasks, such as configuration setup and deployment, workload management, utilization and storage management, as well as maintenance, upgrades and capacity expansion.
Data compression and multi-temperature data management technologies offer another cost-saving tactic because they reduce storage requirements that typically consume a large percentage of the IT budget.
Cluster topology transparency—not requiring applications to be aware of the underlying cluster and database resources and topology— helps accelerate application coding and testing, and can make application developers more productive. And if you switch databases to save money or accelerate processes, you don’t want the expense of having to modify the applications you’re currently using.
Reason 2: A platform for rapid reporting and analytics
You need a fresh, new perspective on data warehousing. DB2 with BLU Acceleration has exactly what you’re looking for. Inside the DB2 database engine, IBM has brought together a complete, multi-workload environment that can help organizations transform their warehousing. After all, getting answers to your sales team in seconds, not hours, can mean the difference between making and breaking a deal.
More people throughout the organization now rely on data-driven insight to do their jobs. To enable this insight, data scientists and business analysts must have an environment that allows them to explore, investigate, experiment, study, scrutinize and discover new or emerging patterns or trends that will affect their company’s markets and revenues.
At the same time, your line-of-business users need quick answers to their business questions. This critical group—including executives, customer-facing personnel and mid-level managers—depends on access to business intelligence so they can make timely decisions, understand their business and identify opportunities before the competition. Meeting these needs requires a database that can deliver analytics very quickly and very simply.
Reason 3: Increased scalability and availability
Keeping your business operating requires data systems that are up and running— all the time. Your customers and partners expect continuous uptime and instantaneous responses, and in this social media age, poor performance can quickly turn into a public relations nightmare.
You need a data system with built-in redundancy, high availability and disaster recovery to seamlessly handle unplanned or external disruptions. And you need transparent database clustering capability to scale up or down based on dynamic business volumes.
Reason 4: Support for new and emerging applications
Customers are price-shopping by phone, comparing ratings on tablets, looking for reviews and experiences on social networking sites and more. All of those touch points represent chances to convince a customer to buy your product and foster loyalty. Therefore, you have to manage the multichannel customer experience accordingly.
This poses a new set of deployment and development challenges for the database infrastructure, which needs to provide the flexibility to serve, capture, store and process a wide variety of information types from different sources. You need a data store that can serve up data when and how it is needed, and capture exactly what is happening at any moment in time.
Reason 5: Flexibility for hybrid environments
Maturing virtualization and cloud capabilities allow you to tap into a broad array of deployment options depending on business requirements. But because not everything can (or should) live in the cloud, many organizations find they are managing resources both on the cloud and on-premises. This reality has led to the rise of the hybrid environment: a blend of data and computing resources from both public cloud sources and on-premises systems.
The hybrid environment is often called “the best of both worlds” for businesses. Cloud-based infrastructures offer an immediate opportunity to avoid the costly investment needed for a robust on-premises computing environment. Structured and unstructured data can be found, processed and managed on the cloud without investing in extra on-premises hardware, while IT can maintain control over privileged data.
If you want to implement a hybrid cloud strategy, you need a database management solution that offers configuration flexibility, data modeling transparency and mixed-workload support. Because the data source matters less in a hybrid environment, how you manage that source—and others across cloud, virtual and on-premises locations—makes the difference when delivering data services. Data and insights must flow seamlessly between environments in an efficient and secure manner to enable access to dynamic storage and application interoperability. These environments must also support the same data warehousing policies and other processes to enable strong integration.
Reason 6: Greater simplicity
The cost of database administration can rival the expense of database software and hardware. The goal is to lower your data management costs by automating administration, increasing storage efficiency, improving performance and simplifying the deployment of virtual appliances. By automating tasks such as configuration setup and deployment, workload monitoring and management, high availability and disaster recovery, you can free up DBAs to focus on new projects.
You probably hear a lot these days about in-memory technologies, like BLU Acceleration, that use new approaches to drive higher levels of analytic performance. What if you could take advantage of these technologies to deliver out-of-the-box high performance for complex queries, achieve groundbreaking storage savings and ultimately flatten the cost-ofanalytics curve?
Every big data source has different characteristics, ranging from frequency and volume to the type and veracity of the data. Add dimensions like governance and security, and now choosing a data and analytics architecture is rife with additional factors to consider: security, governance, connectivity, portability, management and so on. As a result, the right data and analytics solution is often a collection of different systems working together very closely.
IBM Analytics offers an exceptionally deep and broad portfolio of data and analytics technologies and solutions, spanning services, software, research and hybrid cloud. The IBM portfolio includes discovery, reporting and analysis, as well as predictive and cognitive capabilities. It provides enterprise-class Apache Hadoop, expert integrated systems, analytics on streaming data, text and content analytics, and much more, so you can transform the way your organization understands and uses data and content.
These systems typically coexist and interoperate with a relational database management system such as DB2, building a solid data foundation that can extend across any environment. Without a scalable, high-performance data management infrastructure, the results and insights from your analytics endeavors may not be fully comprehensive. This foundation of database and data management technologies powers the analytics and governance tools that feed applications.