Big Data

Up Your Game and Get Measurable Results with Planning Analytics.

September 20, 2016 by MIAWebAdmin

Even under the most supportive circumstances, the application of analytics can feel like a long journey. It can take years to gather data, put the appropriate technologies in place, build the necessary skills, and embed analytical decision-making into key organizational processes.

And once this is all done, many wonder whether they have really uncovered all the value their data has to offer.

In many cases, the circumstances are less than supportive. Senior managers may not be enthusiastic about having their intuition replaced—or at least augmented—with analytics. They may simply not understand the value of an analytics initiative. There may be competing priorities for investment dollars. As with any other change program, managers and organizations can resist analytics programs or may not fully endorse their adoption.

Many companies are focusing more on big data and analytics because they are seeing positive results from trial projects as well as anecdotal evidence from industry colleagues or competitors.

Perhaps most importantly, senior executives are realizing that good data can yield good decisions if it is captured, analyzed, communicated, and acted upon in an agile and efficient fashion. And that realization is at the heart of the analytics dilemma, as one or more of those criteria is often missing from an organization’s analytical capabilities. But those who have cracked the analytics code are reaping its rewards.

Of course, most companies implementing analytics to boost or optimize their marketing and sales performance are applying the analytics capabilities to numerous customer-centric projects and processes. In some instances, this includes aggregating and dissecting huge amounts of unstructured data generated through websites, call centers, social media, and other consumer-facing resources.

The director of commercial analytics for a global health care company recently interviewed by Deloitte stated, “We’re starting to do a better job of identifying who’s doing what and sharing best practices. That’s been a significant step toward a more vertical integration of our data analytics function.”

You can get your organization healthier, more profitable and more confident with a process for improvements based upon the data analytics processes that support savvy, cost-effective decisions.

That’s where and how you will see not only a greater adoption of planning analytics, but far greater results.

Do your research. Ask a lot of questions. Engage a successful, seasoned team of data integration and planning analysts. We cost less and deliver more.

Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven healthcare innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.

Tipping Point: from Planning to Predictive Analytics

August 16, 2016 by MIAWebAdmin

Predictive analytics has become an increasingly hot topic in analytics circles as analysts discuss how we effectively model and use big data, and more people realize that predictive modeling of customer behavior and business scenarios is “the big way to get big value out of data.”

But we’re not anywhere near broad implementation for understandable reasons: time and money top the list, along with a scarcity of talent to transform and match data with priority decisions. But let’s motor on for a couple of minutes. It will be worth it.

There are much bigger potential business benefits for organizations that have invested in predictive analytics software. If a company’s competitors aren’t doing predictive analytics, it has “a great opportunity to get ahead,” Mike Gualtieri of Forrester Research recently reported in a discussion with an online blog. The benefits of predictive analytics are immense and span across various industries.

Virtually all of us are overwhelmed with data and starving for information, but that doesn’t mean it’s just a matter of rolling out the technology and letting analytics teams play around with data. When predictive analytics is done well, the business benefits can be substantial. However, there are some mainly strategic pitfalls. Many companies perform analytics purely for the sake of “doing” analytics, and they aren’t pursuing analytics that are measurable, purposeful, accountable, and understandable by leadership.

Data scientists don’t know it all!

With that in mind, data scientists end up looking for experienced data analysts who have all the required technical skills and also understand their business practices—a combination that can be nearly impossible to find. Difficult, yes. Impossible, not really. An array of other people, from within the business leadership pool and IT, should also play roles in predictive analytics initiatives. When you have the right balance with your team, you’ll end up with a purposeful and thriving analytics process that will produce results.

Companies looking to take advantage of predictive analytics tools also shouldn’t just jump into projects without a plan. You can’t approach predictive analytics the way you do with many other IT projects, so it’s important to think strategically about an implementation upfront and to plot out a formal process that starts with a comprehensive assessment of analytics needs and internal resources and skills. That’s where and how you will see not only a greater adoption of predictive analytics, but far greater results.

Do your research. Ask a lot of questions. Engage a successful, seasoned team of data integration analysts. We cost less and deliver more. Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven healthcare innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.

Curation . . . Just So You Get a Sense of Where Big Data Is and Where It’s Going

July 28, 2016 by MIAWebAdmin

Over the 2014-2019 planning period, annual spending will likely reach 48.6 billion dollars in 2019. A new IDC Special Study examines spending on big data solutions in greater detail across 19 vertical industries and eight big data technologies.

The ever-increasing appetite of businesses to embrace emerging big data-related software and infrastructure technologies while keeping the implementation costs low has led to the creation of a rich ecosystem of new and incumbent suppliers. At the same time, the market opportunity is spurring new investments and M&A activity as incumbent suppliers seek to maintain their relevance by developing comprehensive solutions and new go-to-market paths.

All three major big data submarkets (infrastructure, software, and services) are expected to grow over the next five years. Infrastructure, which consists of computing, networking, storage infrastructure, and other datacenter infrastructure, like security, will grow at a 21.7 percent CAGR; software, which consists of information management, discovery and analytics, and applications software, will grow at a CAGR of 26.2 percent; and services, which includes professional and support services for infrastructure and software, will grow at a CAGR of 22.7 percent. Infrastructure spending will account for roughly one half of all spending throughout the forecast period.

As big data matures, IDC expects its share of the larger Business Analytics market to increase; however, year-over-year growth within the big data market is forecasted to gradually slow. The reduction in overall growth will be largely caused by increased price pressures for infrastructure and higher rates of commercialization of open source software.

The availability and skill level of big data IT and analytics talent will also have a direct impact on the market.

While the business drivers, barriers, and potential benefits from deploying big data initiatives vary from industry to industry, these efforts are primarily focused on delivering a better customer experience, supporting product and service innovation, and optimizing business processes. Common barriers to adoption include security and privacy concerns, as well as the related challenges of collecting, using, and managing customers’ data. A shifting regulatory environment is another area of concern across certain industries.

Get help when and how you need it.

You DO NOT have to bet the farm and the horses to get where you want to go with your data and analytics assets.

Engage a successful, seasoned thought partner to get a value-laden perspective. Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.

Healthcare Analytics: A Quick Review

July 20, 2016 by MIAWebAdmin

Knowing the key stakeholders and users of analytics tools allows IT executives to discover the ways these people apply analytics to meet clinical quality and performance goals. In healthcare organizations, analytics affects almost all decision making, so effective data management and reporting enhances a wide array of roles.

Operational, clinical quality, and performance objectives help define why an organization embarks on quality improvement and business improvement initiatives that will be supported by analytics.

Accurate, relevant, and readily available data form the backbone of all analytics used for decision making. In the age of big data, it is important to understand which data sources are available, the quality of the sources, and how the data can be reported to provide insight into the many workflows and processes that are part of healthcare delivery.

It is also crucial to understand how this data can gauge related factors, including elements such as patient satisfaction expressed via social media.

From business intelligence and reporting to predictive analytics and simulations, there’s a range of analytical tools available to answer pressing business and clinical questions. Selecting the appropriate “tools that fit” ensures that the analytics are the right kind to address user needs and the healthcare organization’s business, clinical quality, and performance challenges.

People are a critical consideration when developing or expanding an analytics plan within a healthcare organization. If your culture supports collaborative analysis and decisions, you won’t feel pressed to bend your data to special interests.

The current, short-term, and long-term analytics needs of a healthcare organization need to be addressed early and often—before it acquires analytics-related capabilities and as it grows into using its big data.

An analytics strategy needs to evolve as analytics demands and capabilities evolve. It must be adjusted as technology becomes better or less expensive. Because of the rapidly changing landscape of healthcare analytics, a healthcare organization leadership team needs to revisit its plan frequently. IT teams also need to regularly check that the plan is up to date and that its execution helps the organization achieve its analytical potential.

Plan. Get your data in shape. Decide. Execute. Measure. Rinse and repeat, always improving.

Get help when you need it.

You DO NOT have to bet the farm and the horses to get where you want to go with your data and analytics assets.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 at to start a friendly, productive conversation.

3 Things that Enhance Your Success from Planning Analytics in Your Hands

July 12, 2016 by MIAWebAdmin

When it comes to cloud planning and analytics, don’t compromise. Planning analytics goes beyond simply automating your planning, budgeting, and forecasting processes. The process provides self-service and self-paced analytics to all of your business users through an innovative planning workspace that empowers business users to seek better business results. It delivers insights automatically from your data and helps you pull ahead of competitors and stay agile for the changes ahead.

Here are some essentials to get you into a position to gain and grow with planning analytics that work.

Well-structured and funded planning analytics provides the following:

Flexible and Scalable: Planning analytics can be leveraged by an individual, team, department, or across the enterprise. Size or budget shouldn’t force you to settle for anything less.
The Cloud Advantage: Using a cloud-based, self-serve software, you get a complete cloud solution with a streamlined and integrated user experience. It provides the entire solution experience for you.
Security: Because you’re smart, your planning analytics solution follows the most demanding security protocols and standards in the industry to protect your data: it’s integrated, streamlined, and secure. Make sure your users respect your data.

We have said this before but it bears repeating for one compelling reason: creating and deploying data analytics is a process, not a destination. And that will continue as we get smarter about our owned data usage and applying big data to make better strategic and tactical decisions.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.

Big Data Needs a Security Big Brother

July 1, 2016 by MIAWebAdmin

A growing number of companies are repurposing funds to use the new breed of cloud technology to store and analyze petabytes of data including web logs, click stream data, and social media content to gain better insights about their customers and business.

As a result, information classification becomes even more critical, and information ownership must be addressed to facilitate any reasonable classification.

Many of these organizations struggle with implementing these concepts, making this a significant challenge. We will need to identify owners for the outputs of big data processes, as well as the raw data.

Thus, data ownership will be distinct from information ownership—perhaps with IT owning the raw data and business unit teams taking responsibility for the outputs.

The challenge of detecting and preventing advanced persistent threats can best be answered by using big-data style analysis. These techniques play a key role in helping detect threats at an early stage, using sophisticated pattern analysis and combining and analyzing multiple data sources. There is also the potential for anomaly identification using feature extraction.

Today, event logs are often ignored unless an incident occurs. Big data provides the opportunity to consolidate and evaluate logs automatically from multiple sources rather than in isolation.

This can provide insight that individual logs cannot, and it can potentially enhance intrusion detection systems (IDS) and intrusion prevention systems (IPS) through continual adjustment and effectively learning “good” and “bad” behaviors.

Integrating information from physical security systems, such as building access controls and even CCTV, could also significantly enhance IDS and IPS to a point where insider attacks and social engineering are factored in to the detection process. This presents the possibility of significantly more advanced detection of cybercrime, fraud, and related criminal activities.

We know that organizational silos reduce the effectiveness of security systems, so businesses must be aware that the potential effectiveness of big data style analysis will be diluted unless these issues are addressed through solid data integration.

You DO NOT have to bet the farm and the horses to get where you want to go with your data and analytics assets.

Engage a successful, seasoned thought partner to get a value-laden perspective. Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven innovation, and it keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call at 512.478.3848 us to start a friendly, productive conversation.

From Foggy Bottom to Deloitte to Us and You – Cybersecurity and Analytics Together!

June 24, 2016 by MIAWebAdmin

With faster access to their data, organizations can follow a data-driven approach to bring in business data and statistical rigor to identify the cause-and-effect relationship between the specific behaviors, competencies, skills and knowledge, and critical business outcomes (e.g. sales, profits, productivity, etc.). But be careful. Be very, very careful not to leave the data door open.

Last year’s super trend—still front and center—continues to grow in importance as more and more organizations experience the losses in value and reputation that can result from a security breach. And we’re not just talking about protecting data. Product design and other IP are also vulnerable to theft and sabotage.

The problem is likely to grow as cybercriminals become more skilled in infiltrating technology architectures and systems that weren’t designed from the ground up through a security lens. Ironically, concerns about cybersecurity could—and perhaps in some cases should—slow the adoption of other trends that drive innovation.

Organizations with a sophisticated approach to cybersecurity are no longer satisfied with locking the doors after the robbery has been committed. International Data Corporation (IDC) estimates that US federal government agencies alone would spend more than 14.5 billion dollars on IT security this year, and the worldwide financial services industry would spend 27.4 billion dollars on information security and fraud prevention. Now that’s a lot.

Going on the Offensive

Organizations such as these are beginning to employ more predictive approaches to threat intelligence and monitoring. In short, they’re going on the offensive. This may mean automated scanning of Internet “chatter” by far-flung groups and individuals who may intend cyberharm.

It may involve analyzing past hacks and breaches to create predictive models of which threats are likely to surface next. In many firms, it also means systematic and continuous probing of the organization’s own defenses to make sure that others don’t find a security hole first.

A Moving Target Creates New Demands

Companies adopting these types of offensive steps will no doubt find that they need new capabilities. Many cyber professionals don’t have the skills to do predictive threat intelligence or predictive analysis of past breaches. At the very least, extensive collaboration between analytics and cyber professionals may be required. And cybersecurity projects will need to rapidly move up the priority list for analytics groups.

You can bring your data flow to its highest and best uses or you can let it languish while you guess and your competitors pass you on the back stretch. And you DO NOT have to bet the farm and the horses to get where you want to go with your data assets.

We are a good match to be your analytics thought partner and trusted virtual team that delivers your data to your dashboards. And we know how to interface with security teams.

Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512-478-3848to start a friendly, productive conversation.

Predictive Analytics Really Does Deliver a Big Bang

June 14, 2016 by MIAWebAdmin

In their recent case studies, a group of data analytics collaborators from Microsoft and two universities discussed where predictive analytics is having the biggest impact and shared new insights on the value of predictive/planning analytics across industries and along value chains.

Here are some key findings from their case studies:

Airlines are particularly interested in predicting mechanical failures in advance so they can reduce flight delays or cancellations.
- Some airlines are able to predict the probability of aircraft being delayed or canceled in the future based on relevant data sources, such as maintenance history and flight route information.
- A machine-learning solution based on historical data and applied in real time predicts the type of mechanical issue that will cause a flight delay or cancellation within the next 24 hours. This allows the airlines to take maintenance actions while the aircrafts are being serviced, thus preventing possible delays or cancellations.
Similar predictive-maintenance solutions are also built in other industries. For example, in the oil and gas industry, companies are tracking real-time telemetry data to predict the remaining useful life of an aircraft engine and employing the data to predict the failure of electric submersible pumps used to extract crude oil.

These cases help highlight a few general principles:

The value derived from the analytics piece can greatly exceed the cost of the infrastructure. This indicates that there will be strong growth in big data consulting services and specialized roles within firms.
Big data is less about size and more about introducing fundamentally new information to prediction and decision processes. This information matters most when existing data sources are insufficient to provide accurate or actionable predictions due to small sample sizes or coarseness of historical sales (e.g. small effective regions, niche products, new offerings, etc.).
The new information is often buried in detailed and relatively unstructured data logs (known as a “data lake”), and techniques from computer science are needed to extract insights from it. To leverage big data, it is vital to have talented data engineers, statisticians, and behavioral scientists working in tandem. The term “data scientist” is often used to refer to someone who has these three skills, but in our experience, single individuals rarely possess all three.

Predictive analytics also has the potential to be applied in ways that disrupt existing processes and create radically new applications. For example, machine-learning models that take massive data sets as inputs, coupled with clever designs that account for patient histories, have the potential to revolutionize how certain diseases are diagnosed and treated.

Another example involves matching distributed electricity generation (e.g. solar panels on roofs) to localized electricity demand. Huge value is unlocked by equating electricity supply and demand with more efficient generation.

The immense value of predicting demand more accurately, creating better pricing, and performing predictive maintenance easily justifies large firms’ investments in big data infrastructure and data science.

The value of radically new applications is challenging to understand and naturally causes speculation. It is reasonable to expect losses for many firms, due to uncertain and higher-risk investments, with a few firms earning spectacular profits.

Two of the principles we have listed above focus on the need for (a) consultive thought partners to help organize the analytics infrastructure and (b) data scientists and analysts to bring new data combinations and relationships to decision-makers.

A truly competitive enterprise needs a contemporary, on-call set of analytics that is supported by big data and everything else and is based on a streaming architecture.

Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512-478-3848 to start a friendly, productive conversation.

Data-Based Analytics Accelerates Performance and Innovation

June 8, 2016 by MIAWebAdmin

The data used needs to be function- or workgroup-specific. For example, if “sales dollars” has been identified as a critical business outcome for the sales team, you will incorporate and statistically link the sales leader’s/rep’s 360 assessment data to the “sales dollars” outcome.

Using advanced analytics (e.g. structural equation modeling) as your methodology, you can identify which components of the 360 assessment (i.e. behaviors, knowledge, skills, and abilities) are key drivers of specific business outcomes for the various functions or workgroups being analyzed.

Specifically, the results of the analysis indicate the differential impact that each of the competencies evaluated using the assessment has on the key business outcome (i.e. how much improvements on each competency will contribute to improvements in the overall business outcome).

Discovering the competencies most responsible for business outcomes allows HR leaders to make data-driven decisions regarding where to invest developmental efforts and dollars and how to demonstrate a return on these investments on the back end.

Innovation has always been a key force in changing business and society. More and more, innovation is occurring as the result of aggregating and analyzing data to create new products and services. The Internet of Things (IoT) is evolving quickly from the arena of interesting gadgets to include tracking people to form new business models (think Uber) and to influence people’s behaviors.

A truly competitive enterprise needs a contemporary, on-call set of analytics that supports big data and everything else and is based on a streaming architecture. This is the era of visual analytics at the speed of thought. Performance improves and business outcomes accelerate.

You DO NOT have to bet the farm and the horses to get where you want to go with your data assets. We are a good match to be your analytics thought partner and trusted virtual team who delivers your data to your dashboards.

Our Austin, Texas location puts us at the epicenter of planning analytics and data-driven innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.

Why Are You Sitting in the Middle of the Road to Analytics Excellence?

May 18, 2016 by MIAWebAdmin

Our assessments of situations do not need to be precisely accurate. They just need to be directionally right so that subsequent decisions about things like strategy, tactics, and investment levels have a much better chance of success.

Making an assessment that is directionally wrong, such as misreading a rising market and leaving it wide open to competitors, can obviously cause problems.

However, I would suggest that a business or healthcare delivery system is in even greater danger when its leaders use the prevailing uncertainty as an excuse for not making decisions. A rabbit that sits out in the highway dazed by the headlights is eventually going to be roadkill, but one that thinks there may be a gap in the traffic might live to see another day.

See where I’m going with this best practices concept?

It can be tempting to just go out and buy big data analytics software, thinking it will be the answer to your company’s business needs. But big data analytics technologies on their own aren’t sufficient to handle the task.

Well-planned analytical processes and people with the data-manipulation talent and skills needed to leverage the technologies are essential to carry out an effective big data analytics initiative.

Buying additional tools beyond an organization’s existing market intelligence and analytics applications may not even be necessary, depending on a project’s or business unit’s particular business goals.

As big data analytics tools and processes mature, organizations face additional challenges but can benefit from their own experiences, technology improvements, and helpful discoveries by other users and analysts. And from an analytics thought partner.

Big data environments are becoming a friendlier place for analytics because of upgraded platforms and a better understanding of data analysis tools. Ask your analytics thought partner to dig deeper into the evolving world of big data analytics.

Managing information and giving you evidence-based perspectives on your operational and human asset-management challenges is what we do . . . all day, every day.

Our Austin, Texas location keeps us at the epicenter of software engineering innovation and keeps Management Information Analysis’ global capabilities at the forefront of our industry.

Call us to start a friendly and productive conversation. 512-478-3848.