Tips for choosing data management solutions: Consider roadmaps, partners, and talent

IDC’s report, “Choosing the Right Database Technology in the Age of Digital Transformation” highlights the expanse of data management options as well as how that can cause confusion. Having clarity when looking to build new data applications or modernize workloads already in existence is essential. Three of the tips they provide to help illuminate the right solution for your needs are to select solutions that have:

  • Roadmaps which fit your future plans
  • Partners that help round out the overall solution
  • Technologies with a large talent pool internally and externally

Of course, this is more easily said than done. IDC is quick to dismiss jack-of-all-trade types of solutions in favor of specialized solutions across edge and IoT, data science, streaming, advanced analytics, data lakes, data warehouses, and transactional processing. To help, we’ll explore how to follow IDC’s advice while still selecting technologies suited to a business’s individual needs.

Roadmaps and the future

Dynamic and shifting markets can make predicting future technology needs spotty if not impossible. This makes finding solutions with a good roadmap or path forward even more essential. Make sure that the solution being chosen is structured to grow as new technologies are created and has a clear path forward on sure bets like Artificial Intelligence (AI).

A good example of this can be found in IBM Cloud Pak for Data. Many of IBM’s specialized data management solutions are available through Cloud Pak for Data, including a database, data warehouse, and fast data option. A business can add additional capabilities easily without changing the underlying structure. Moreover, technologies across the Journey to AI can be added such as IBM Watson OpenScale. Whether data management related or otherwise, all can be added by purchasing additional virtual processor cores (VPCs). It is also hardware and cloud agnostic – built on Red Hat OpenShift Container Platform, it can run wherever Red Hat and Linux containers are supported. If future demands necessitate it, selecting new locations will be much simpler.

One shift that can be counted on is the rise of AI. Data management solutions should be both built for AI, making the development of AI applications more effective and efficient, as well as powered by AI, using AI to improve the data management process itself. Data virtualization and support for popular data science languages are two components of this which will be discussed later. However, features like natural language querying to explore data more easily and confidence-based querying to provide probability of accuracy alongside responses have the power to help improve insights in the upcoming years. Data management solutions should have a plan to incorporate these features.

Partner ecosystem

The partner ecosystem is critical to expanding the functionality of data management solutions. Because companies may specialize in different areas of data management, having strong partner relationships opens access to additional functionality in a tightly integrated way.

For example, companies that partner with Cloudera can offer robust data lake implementations alongside what their own database, data warehouse, or other specialties. Similarly, open source specialists like MongoDB can lend their own expertise where NoSQL is required.

However, these companies should not rely fully on their partners; they must provide additive value on top of what the partner delivers. Where Cloudera is concerned, solutions like IBM Db2 Big SQL add value through improved SQL connectivity. Open source solutions can be bolstered by expert support that can speak to both the primary solution, the partner solution, and how both integrate.

Unfortunately, all of the preferred data management options may not be partnered with one another. In these cases, data virtualization, which provides a single view of and access to all data sources without an ETL (extract, transform, load) process, is vital. Businesses can choose the specialized solutions they want and continue to have access to it no matter where the data happens to live.

Internal and external talent

The best way for data management to ensure that there is enough talent available to leverage the technology is to meet the users on their own terms. In other words, they should allow them to use the languages, data formats, and libraries that they are familiar with.

For data scientists and application developers, those languages include Python, JSON, GO, Ruby, PHP, Java, Node.js, Sequelize, and Jupyter Notebooks. Offering these options helps existing employees continue using what they already know, opens up a larger pool of external talent which can be hired, and makes it more likely that code examples made by peers will be available.

SQL is another common language that should be leveraged to make the integration of newer technologies like Graph and blockchain easier. Previously, data would need to be extracted out of relational systems and put into a graph database prior to analysis. However, it is now possible to run graph applications from relational databases and query graph data directly using SQL. This helps eliminate wasted time and potentially reduce costs that would have been incurred with ETL.

With blockchain, data that was previously hard to access by design can now be used with SQL without altering the blockchain itself. Therefore, it can easily be used alongside other data sources to deliver a more complete set of insights. Consider combining blockchain data with weather data. Doing so may help companies transporting goods more accurately determine why there may have been delays on route. And, as with Graph, ETL is no longer necessary in order to save time and costs.

Discover additional tips by reading IDC’s report “Choosing the Right Database Technology in the Age of Digital Transformation” or learn more about a forward-looking database by reading the eBook, Db2: The AI Database.

If you have questions about these tips or other hybrid data management topics, our experts would also be happy to have a free, 30-minute discussion with you one-on-one.

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The technical advancements behind Db2

We are pleased to announce that IBM’s Operational Database Management Systems (OPDBMS) was recognized as a 2020 Gartner Peer Insights Customers’ Choice. One of our greatest joys is delivering the advanced data management capabilities clients need to fit the unique needs of their business to drive success.

Customer reviews that contributed to this distinction focused on the implementation strategy, evaluation and contracting, integration and deployment, service and support, and overall capabilities throughout IBM’s OPDBMS suite of offerings and extended data management offerings.

IBM is continuing to double down on the benefits of its offerings with the introduction of Cloud Pak for Data, a focus on AI, and the capability to flexibly access data across an even wider range of sources.

Ease of use

Customer comments that contributed to the Gartner Peer Insights Customers’ Choice distinction indicated a “willingness to recommend” IBM’s OPDBMS. We think this is in part due to the trained teams of experts that partner with our clients to ensure the right fit. This extends into documentation and support that educates a company on solutions or best practices to make the implementation go more smoothly. And should there ever be a question or problem, a robust community of users is available to provide advice.

An example of IBM’s OPDBMS’s ease of use is the IBM Db2 database; Db2 is now even easier to deploy and implement due to recent simplification efforts. There are three versions of Db2 which are all available with the full set of features. Start with the free community edition to test the performance with 4 cores and 16 GB RAM. Since all three editions share a common download if you decide you want to upgrade to the standard edition with 16 cores and 128 GB RAM, or the advanced edition with unlimited cores and RAM, you can do so without app changes or migration. A single command line is used to apply the new license, and each version is based on hardware limits, you can’t unintentionally overshoot them.

Performance and optimization

The list of high-powered capabilities within IBM’s OPDBMS is nearly endless. For example, the compression and data skipping technologies allow businesses to preserve valuable space and avoid processing data not needed for a particular query. This saves both storage costs and time as insights can be delivered faster. Optimization features were also praised by respondents with some going so far as to list it as what they like about Db2 the most.

To deliver even greater performance and optimization, IBM is pursuing a strong focus on AI. Built for and powered by AI, Db2 is driving additional ways to speed competitive advantage. Optimizers are a good example of the “powered by AI” technologies. Where normal query optimizers may continue to suggest the same query path even after it proves to be less effective than hoped, a machine learning query optimizer can learn from experience, mimicking neural network patterns. This helps it constantly improve as opposed to optimizing at intervals.

Built for AI technologies will help users of IBM OPDBMS to take advantage of exciting new opportunities around graph and blockchain. Previously, data would need to be taken out of a relational database and put into a graph database adding cost and time through duplication and migration. Db2 Graph integrates graph functionality into the relational database, eliminating the need to duplicate data, providing a different level of insight than SQL analytics, and providing the ability to update graphs in real-time for transaction processing and analytics. Similarly, the Db2 Blockchain connector links blockchain data stores to Db2 databases opening it up to analysis and use in AI apps.

Integration

Access to as much data as possible helps drive better insight by enabling more perspectives to be taken into account. This is doubly true when building out a corpus of data for AI. The way to achieve it is through strong connections between all data sources no matter data management solution they sit within or how they’ve been deployed: on premises, on private cloud, or on public cloud. A hybrid, multicloud approach must be taken.

Using Db2 as part of IBM Cloud Pak for Data makes this much easier. It uses data virtualization, achieved by combining data federation and an abstraction layer, to let users interact with all data from a single access point. All data in this case includes all formats, types, sizes, and locations. Again, the lack of migration saves time and the single access point makes governance much easier by isolating a single point to watch. Cloud Pak for Data is also built on Red Hat OpenShift Container Platform allowing it and Db2 to be run on any cloud supporting RedHat. Moreover, many other solutions across IBM’s AI Ladder are included within Cloud Pak for Data such as Watson Studio Open Scale and can be activated by purchasing more VPCs.

Learn more about the features customers appreciate in Db2 by reading the ebook, Db2: The AI Database or start your free trial to experience it for yourself.

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Gartner Peer Insights Customers’ Choice constitute the subjective opinions of individual end-user reviews, ratings, and data applied against a documented methodology; they neither represent the views of, nor constitute an endorsement by, Gartner or its affiliates.

Creating trusted COVID-19 data for communities

In these rapidly changing times, we all need to get the best information available to make better informed decisions. Between news reports, adjusting to social distancing practices, and other daily adjustments, many of us face a deluge of incoming information. IBM is making it easier for people around the world to stay up to date on COVID-19 in their vicinity as demand for the latest news and statistics surges week-to-week.

Using IBM Cognos Analytics, IBM has developed a new, interactive global dashboard to show the spread of COVID-19 across the world. The COVID-19 data reflected in this dashboard is pulled from state and local governments and the World Health Organization.

The dashboard is designed to give the general public, researchers and even government officials detailed, localized and current information about COVID-19. 

The dashboard responds to growing public demand for information about the virus. For example, visits to the top U.S. government websites with information about COVID-19 increased by 79 percent the week of March 9, compared with a week earlier—and by 425 percent compared with the week of Jan. 6, before many people were tracking news of the virus.

Above and beyond

The dashboard aims to offer a deeper level of functionality and detail, which could be especially useful to researchers and public health officials.

In addition to the data the dashboard provides, it also offers multiple ways to summarize data. For example, users can view statistics on active cases, deaths and recovered patients by country; number of cases by region; and historical trends. IBM is also considering developing additional tools for researchers.

Alongside the Cognos COVID-19 dashboard, IBM has developed an Incidents Map on the IBM Weather Channel app and weather.com. The Incidents Map provides data by state, as well as county-level data. It includes trend graphs at the state level in the U.S., as well as the latest COVID-19 news and video from The Weather Channel editorial team. Public health information and patient education materials are also provided.

Both of the new IBM tools pull data from the World Health Organization and U.S. state and county government sources. In the future, IBM is planning to also collect data from national health departments outside of the U.S.

Necessity is the mother of all invention

Created in less than a week, the Incidents Map and Cognos COVID-19 dashboard are the result of extensive IBM teamwork and are in keeping with a key IBM objective: providing trusted information for all.

Experts from throughout the company collaborated around the clock using agile development methods while working from home. Instead of gathering around a whiteboard, developers and other experts worked remotely, using IBM digital collaboration tools and teleconferencing.

Stay positive

With the sudden change in our everyday lives, we must always remember that whenever a challenge arises, we should answer the call as a community. The dashboard detailed in this piece is an example of how quickly people with widely different talents can come together for a common goal that benefits everyone.

Explore the Cognos COVID-19 dashboard.

Explore the Incidents Map.

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IBM SPSS Statistics free trial extended through June 15 due to pandemic

We recognize that these are difficult times. In response to the worldwide pandemic, IBM will be extending the SPSS Statistics Subscription trial for active and new accounts through June 15. This will allow our users time to adjust to this dynamic and unprecedented situation.

To sign up for a free trial, click here: http://ibm.biz/spsstrial2020

With many schools facing campus closures, we recognize the need to access learning tools online. To address this, IBM will also amend current SPSS Statistics Campus agreements with IBM to allow virtual computing and/or home use of SPSS Statistics at no additional cost.

For any questions regarding this contact our team at: spssadmn@us.ibm.com.

The move is yet another example of IBM’s long-standing approach in supporting the academic and analytic research community. The company also offers Watson Studio Desktop subscription through Academic initiative.

To sign up, click here: ibm.biz/academic

Our commitment to the research and academic community continues to stay strong as we support the transition through these uncertain circumstances. As a team, we will emerge stronger together.

On behalf of IBM and the SPSS Statistics team, we hope you are all staying safe and healthy.

Ryan Dolley partners up to facilitate change with analytics

This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.

Immediately following his client, Heather Gardner, for a filmed interview at Data and AI Forum in Miami, we meet Ryan Dolley of PMsquare, a full-service IBM Analytics business partner. Ryan is the Director of Technology, and PMsquare has a longstanding relationship with Alliant Energy, a utility company based in Madison, Wisconsin. Ryan says, “we guide companies through the whole process of using IBM Analytics.” Ryan specializes in helping people understand the power of analytics solutions, how they can license and, deploy them, and he collaborates on project design and delivery.

 Ryan Dolley partners up to facilitate change with analyticsHelping clients rethink BI

For more than a decade, PMsquare has helped Alliant Energy get the most from their investment in IBM Cognos Analytics. “We help [Alliant Energy] look at the way they’ve traditionally done things, and then build bridges into the future, so they can build a more agile and self-service analytics workflow,” says Ryan.

Over the years, Ryan realized that Alliant Energy had settled into a “pretty rigid” set of business intelligence processes and needed to rethink its approach. The first step to solving this problem was to gather information. Ryan decided to interview their Business Intelligence (BI) team to figure out what set of capabilities would be most valuable, which users should have access to them, and how to best deploy them. He describes his findings as “there were two sides to the coin: making sure we were satisfying users while ensuring the BI team could support the processes. If you don’t have buy-in on both sides, you won’t have a successful deployment.”

Leveraging IBM’s full spectrum of data solutions

Ryan discusses the advantages of using IBM’s suite of analytic software, “we’ve also been using IBM Planning Analytics powered by TM1, which has great integration with Cognos Analytics.” He goes on to describe his experience partnering with IBM and how it helps his clients reach their goals. Ryan states, “the thing about IBM that sets them apart from everybody else is the breadth of the technology that they bring to bear… no matter what type of problem you’re trying to solve with data, IBM has a solution tailored to it. And all of those solutions work together on a common platform. So, you can bring data into the IBM analytics platform, expose them through a variety of endpoints, and be confident that you’re going to have a unified governance layer, knowledge catalog, and data architecture.”

Ryan concludes by comparing other solutions and their offerings, “when you look at IBM versus other players in the analytics market, there are lots of people who do one slice of it very well, but there’s only one company that does the whole thing.”

Combining traditional reporting with agile visualizations

For a company with such a deep archive of data, it was important to maintain access to historical processes and reports. Alliant Energy’s needs are a combination of classic data warehousing and BI with a more modern, highly visual analytics methodology. “In our approach, we didn’t want to overturn the applecart. We wanted to maintain the foundational aspects of reporting, but also bring in the ability for users to upload their own spreadsheets to Cognos Analytics, connect them to the enterprise data warehouse, and build their own visualizations,” says Ryan.

PMsquare went into their partnership with the idea that they would rethink the way Alliant was doing BI, and bring in more modern processes. To this point, Ryan adds, “but we’re not going to chuck out everything they’d built up over 20 years, because a lot of that is [still] really valuable.”

Building a flexible foundation for a changing industry

As an energy provider, Alliant Energy needs to anticipate and respond to changes in the utility industry such as distributed power generation, solar energy, and renewables. According to Ryan, “Alliant needs an expandable analytics platform to face its future data challenges.” Ryan believes that by partnering with PMsquare and IBM, Alliant is building a rock-solid foundation to build on, “no matter what happens with the utility industry — for example, with streaming data, and Internet of Things data coming online — we know we can help them adapt.”

Ryan is excited about the opportunities these changes will bring. “I see a lot of convergences happening between technologies,” he says. “For example, in some places you build on the data from IBM Cloud Paks, then add Cognos or Planning Analytics to your portfolio, and it all fits together… it’s like a really fun data-based set of LEGO,” says Ryan with a laugh.

Being an Analytics Hero: Teaming up to save the day

When asked about his partnership with Alliant Energy, Heather Gardner chimes in, “knowing that [Ryan] is there to help me with my skillset, training, get the environment tuned, and especially helping me celebrate success, [he’s] helped me become a Hero.” For all that he’s done for Heather and Alliant Energy, Ryan is recognized as an Analytics Hero. By teaming up with his client, using his talent and connections he, like Heather, is able to soar to great heights.

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Health Care Access Now: How trust and trusted data help reduce healthcare inequality

The numbers are alarming. In 2018, the World Health Organization, reported the United States had the sixth highest number of preterm births in the world.

In the United States today, 10 percent of children are born premature—three or more weeks before their due date. According to the 2019 March of Dimes Report Card, premature birth in the State of Ohio occurs at a rate of 10.3 percent. In Hamilton County, which includes Cincinnati, the percentage climbs almost one percentage point. And the statistics become more disturbing from there.

In 2019, black women in Ohio are 49 percent more likely to experience a preterm birth than their Caucasian, Asian and Hispanic neighbors. In 2017, deaths among African American babies in Hamilton County were three times greater than non-African American babies. Many of these deaths occurred because women delayed prenatal care or experienced a preterm birth.

But these numbers could go down as our Certified Community Healthcare Workers (C-CHW) are combatting these and other healthcare challenges facing our county.

Health Care Access Now: How trust and trusted data help reduce healthcare inequalityEmpowering the local community for support

Our organization, Health Care Access Now (HCAN), was launched by community leaders in 2009 to improve the overall health status of our most vulnerable residents in Greater Cincinnati. Our Certified Community Health Workers are the boots on the ground focused on social determinants of health and the barriers a client is facing. We pair our workers with those who are likely to have poor health outcomes due to inconsistent medical care, challenges accessing behavioral and specialty care, or lack support in successfully managing chronic diseases. More than 55 percent of HCAN’s clients are expectant mothers while other clients include the elderly.

Essential to the mission are three ingredients: trust, skills, and data.

Lending a hand, earning trust

Trust is a critical element to HCAN and its Certified Community Health Workers’ success. Clients are often referred to HCAN by Medicaid health plans, physicians, United Way 211, or others who have benefited from the service. During the intake process, clients first provide personal data and medical history then share their experiences and challenges. “Sometimes it’s hard to break through to clients,” Giacoma Telich, Certified Community Health Worker at HCAN says. “But, I’ve been through a lot on my own, and I open up about my experiences. It doesn’t matter where you start from; it matters where you finish.” Clients trust that their C-CHW will judiciously use the information they shared to find and deliver the services they need. The real trust begins as they see the many ways their C-CHW shows up in their life.

Separate paths converge into one journey

Community Health Worker isn’t a college major. These dedicated individuals develop their skills through facing life’s obstacles and professional training. They live in the communities they serve. They are in touch with the needs of the community. They may have faced similar challenges. Their life experience, determination and compassion help them relate to their clients on a personal level. Telich describes her encounters with clients, “I share my stories and show my vulnerability. I know what it’s like not to have lights on.”

For clients facing pregnancy, C-CHWs like Telich skillfully provide their clients with a network of community resources to support a healthy pregnancy and delivery as well as continued access to ongoing medical care. Enabling access to available community services is only part of the story. The C-CHWs need near real time access to be there for their clients in their hour of need.

Delivering trusted data when it matters most

While trusted human connection and skills empower the C-CHW, data provides the fuel. HCAN relies on a centralized information management and reporting system that pushes alerts to C-CHWs when a client has a healthcare event and is admitted for care.

We rely on the information received from healthcare providers in order to do a good job for the clients. Having partnerships with organizations like The Health Collaborative, a Cincinnati-based electronic medical data exchange, quickly provides us with data we can trust thanks to their Master Patient Index, built on IBM’s InfoSphere Master Data Management. Through our integration with their system, client alerts pop into the C-CHWs’ case dashboard. This data enables them to reach out to that client very quickly. 

Integrating data and decision-making leads to new skills

As their data analytics skills increase, some C-CHWs have expanded their career paths. For example, our data support specialist started as a certified community health worker. She transitioned to the role because her attention to detail, understanding of community and knowledge of data were the perfect fit. She can see and understand the data from the client’s side, from the community health worker’s perspective, and she catches issues or errors that a person without her experience wouldn’t see. Providing new opportunities for our employees, like they offer to our clients was an unexpected and positive benefit of our data reporting and alerts system.

Improving outcomes throughout the community

Why should you care about healthcare equality for expectant mothers? Addressing social determinants and providing prenatal care is not only good for the expectant mother and her child, it helps the community at large. The effects and costs of premature births are far reaching. For example, complications from preterm birth are the leading cause of death among children under five years of age, responsible for approximately 1 million deaths in 2015. Many survivors face a lifetime of disability, including learning disabilities and visual, and hearing problems. Individuals may also be afflicted with cerebral palsy. The costs associated with care and support strain struggling communities.

In 2007, the Institute of Medicine reported that the cost associated with premature birth in the United States was USD 26.2 billion each year. In today’s dollars, it is estimated to top USD 32 billion. For example:

  • 611 million for early intervention services, programs for children under age three with disabilities and developmental delays.
  • 1.1 billion for special education services specially designed for children with disabilities ages three through 21.
  • 16.9 billion in medical and health care costs for the baby.
  • 1.9 billion in labor and delivery costs for mom.
  • 5.7 billion in lost work and pay for people born prematurely.

The whole person care provided by HCAN and its C-CHW addresses the blockers that effect the most vulnerable in their community. Building trust, depending on skills of their C-CHWs and relying on accurate and timely data is making an impact to Cincinnati and Hamilton County, one individual at a time.

For more information on IBM InfoSphere Master Data Management solutions, visit: https://www.ibm.com/analytics/master-data-management.

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I feel the need to stream: the impact of continuous intelligence

Staying at the forefront of digital transformation means embracing constant change. It’s about staying nimble to customer demands, tapping into the pulse of a shifting market, and taking actions on insights as they’re developed. All of this can be made possible through continuous intelligence (CI).

Grounded in real-time analytics, CI allows companies to make informed, of-the-moment decisions as events occur. Integrating historical and streaming data, CI delivers a more complete picture with insights into not only what’s happening now, but why. Unlike classic business intelligence, it incorporates machine learning and AI at the core to form predictive analysis and automate decision support. When infused into business processes across hybrid and multicloud environments, CI can help companies streamline operations, detect and fix problems before they emerge, save resources, spike ROI, and ultimately improve the bottom line. In fact, the case for continuous intelligence is so strong that Gartner estimates more than half of major new business systems will incorporate CI by 2022.

IBM has worked with clients across a wide range of industries to deliver impactful business results with CI. From improving disaster relief to creating smart Japanese vending machines, these industry uses cases are a testament to CI’s vast potential to transform and innovate. Hear stories from Mike Beddow and Cathy Reese, two sales leaders at IBM’s Global Business Services, in conversation with RTInsights.com:

Continuous intelligence in the public sector

When natural disasters strike, one of the top priorities for the government is to safely and quickly deploy emergency resources. Beddow takes us through the true story of a botched effort for disaster relief by a local state government and how that incident inspired a new wave of predictive analytics to aid emergency resource allocation. Cloud Pak for Data, IBM’s leading data and AI platform, helps the state determine, for example, whether a certain district will need more snowplows than others and reallocate in real-time as necessary.

Continuous intelligence in the transportation, utility, and retail industries

“Everyone’s looking for the next new business model,” says Reese, and CI plays a crucial role in helping companies innovate. In the transportation industry, shipping trucks can now track the types of traffic that drive by and open up new ad revenue streams from sponsors who seek hyper-targeted advertising at the side of the trucks. In retail, Japanese vending machines can now collect data from foot traffic, weather, time of day and more to determine what kind of items to offer—such as cold drinks on a hot day, warm soup on a cold day, etc. Utility companies can adjust their vegetation management strategies by detecting where fire is most likely to occur, drawing from historical and real-time data such as wind speed and humidity.

Continuous intelligence in healthcare

When sepsis—the body’s life-threatening reaction to infection—occurs in a patient, detection often happens too late. Beddow works with clients that are using CI to provide early detection of sepsis and outcome-based care. This is one example of how CI can help hospitals shift away from a fee-for-service model (i.e. “how many patients are seen”) to one that can predict real-time outcomes based on individualized treatments (i.e. “which prescriptions actually led to faster recovery times?”).

Continuous intelligence in the insurance industry

CI is helping an Italian insurance company innovate with telematics (collection and/or transmission of data from a vehicle at rest or in motion). By matching streaming telematics data with claims data, the company can process claims much more quickly. It can also detect real-time events, such as accidents or fraud, through IBM Cloud Pak for Data’s integrated platform that brings together siloed sources of historical, streaming, customer service, and policy data.

Continuous intelligence in the chemical industry

Data around chemical defects is not the kind that can wait to be batched in the next 24 hours. It needs to be spotted and fixed right away, and Reese is working with one company to do just that. By using IBM Cloud Pak for Data to build a flexible information architecture, the company can add on different use cases as needed to achieve quick wins and alert clients who may be impacted by potentially hazardous chemical defects.

To learn more about how IBM Cloud Pak for Data supports continuous intelligence, read our ebook Successful Continuous Intelligence Across Various Industries or visit our website.

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