Tag Archive for: Cloud

What causes advanced analytics and AI initiatives to fail? Some of the main reasons include not having the right compute infrastructure, not having a foundation of trusted data, choosing the wrong solution or technology for the task at hand and lacking staff with the right skill sets. Many organizations deploy minimum valuable products (MVP) but fail to successfully scale them across their business. The solution? Outsourcing elements of analytics and AI strategy in order to ensure success and gain true value.

64% of leaders surveyed said they lacked the in-house capabilities to support data-and-analytics initiatives. 

It’s essential to implement a data-driven culture across your organization if you’re looking to adopt advanced analytics. One of the keys to a data-driven culture is having staff with the correct skills that align with your initiatives. In our study, 64 out of 100 leaders identified a lack of staff with the right skills as a barrier to adopting advanced analytics within their organization. Even for organizations that do have the correct skill sets, retaining that talent is also a barrier they face. This is where outsourcing comes in.

Borrowing the right talent for only as long as you need it can be an efficient path forward.

Outsourcing parts of your analytics journey means you’re going directly to the experts in the field. Instead of spending time and money searching for the right person both technically and culturally, outsourcing allows you to “borrow” that talent. The company you choose to outsource to has already vetted their employees and done the heavy lifting for you. With outsourcing, you can trust that your organization is working with professionals with the skill sets you need.

Aside from securing professionals with the correct skill sets, there are plenty of other benefits to outsourcing your organization’s analytics needs. Professionals with the skill sets necessary for advanced analytics and AI initiatives can be very expensive. Outsourcing provides a cost-effective option to achieve the same goal. Rather than paying the full-time salary and benefits of a data science or analytics professional, an organization can test the value of these kinds of ventures on a project to project basis and then evaluate the need for a long-term investment.

Freeing full-time employees to make the most of their institutional knowledge.

Another benefit of outsourcing analytics is the increased productivity and focus of your organization’s full-time employees. By outsourcing your organization’s analytics, your full-time employees will naturally have more bandwidth to focus on other high priority tasks and initiatives. Rather than spending their time on what the outsourcing company is now working on, the full-time employees can dedicate their time to work on things that may require institutional knowledge or other tasks that are not suited for a third party. It’s a win-win situation for your organization – your analytics needs are being handled and your full-time staff is more focused and still productive.

There are many areas of analytics that an organization can outsource. These areas include but are not limited to viability assessments, prioritization of use cases, managing the ongoing monitoring, performance, maintenance and governance of a solution and implementing and deploying an MVP or use case.  In the words of Brian Platt, Ironside’s Practice Director of Data Science, “A partner with advanced analytics and data science capabilities can rapidly address AI challenges with skills and experience that are hard to develop in-house.”

Mid-tier organizations need the right talent and tools to successfully realize the value of their data and analytics framework in the cloud. The Corinium report shows that many companies are increasingly prepared to work with cloud consulting partners to access the skills and capabilities they require. 

 Areas that mid-market leaders consider outsourcing.

Overall, more and more data leaders are turning to outsourcing to help fill the gaps and expedite their organization’s analytics journey. Outsourcing services can help your organization reach analytics goals in many different areas, not just AI and Advanced Analytics. 

Organizations rely on outsourcing in key areas like these:

  • Developing a data and analytics cloud roadmap
  • Assessing advanced analytics use cases (figure shows 68% would consider outsourcing)
  • Implementation and deployment of a MVP, or use case (figure shows 43% outsource)
  • Developing and maintaining data pipelines
  • Documenting and assessing your BI and overall analytics environment(s)
  • Migrating your reporting environment from one technology to another
  • Overall management and monitoring of analytics or AI platform (figure shows 42% are already outsourcing)

When your company plugs into the right skill sets and processes, there’s nothing between you and a successful data-and-analytics transformation.

Take a look at the full whitepaper to learn more: Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Contact Ironside Group today to accelerate your Advanced Analytics and AI Strategies.

Midmarket companies use Advanced Analytics and AI to automate processes, glean strategic insights and make predictions at scale such as:

  • Marketing – What is the next best offer for this client? 
  • Customer churn – Will this customer churn soon?
  • Predictive maintenance – When will this machine or vehicle fail?
  • Insurance- Will this person file a large claim?
  • Healthcare – Will this person develop diabetes?

Companies can wait until their competitors, or new entrants leverage AI in their industry, or they can start the process now.  There’s no doubt that the coming years will see AI applied to ever-increasing processes in the organization.  The urgency is to start reaping the benefits before widespread adoption in your industry occurs. 

The good news is that midmarket companies are still in the early stages of large-scale deployment of AI projects.  A recent survey by Corinium Intelligence (Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them) found that only 4% of respondents say their advanced analytics models and self-service tooling are fully scaled and integrated with business processes across the organization.

However, midmarket companies are actively scaling and experimenting with AI and Advanced Analytics in their business processes.  The survey found that 53% are creating MVPs (Minimum Viable Products) and 36% are in the process of scaling advanced analytics and AI, well on their way to deployment.

AI adoption will transform business models over 2-5 years. The time to start is now.

What challenges do midmarket companies face as they define, build and deploy Advanced Analytics and AI technologies in their companies?

The Corinium Intelligence survey asked mid-market companies about the biggest mistakes they saw or experienced in deploying Advanced Analytics and AI.  This survey of 100 data and analytics leaders from the financial services, insurance, telecoms, retail, and manufacturing sectors highlights the challenges enterprises face at each step of the data modernization journey – from designing the right data architecture to incorporating AI in business processes for competitive advantage.

59% of respondents cited inadequate data and compute infrastructure as the leading impediment.  Choosing the right technologies, hiring the right skill sets and proactively investing in change management are the next three sources of mistakes on the path to utilizing the AI/Advanced Analytics. 

Choosing the wrong analytics or AI technology solutions can result in setbacks later on. It’s important to carefully consider the various analytics and AI solutions that are available and choose the one that best meets the needs of the organization.

Successful analytics and AI projects require a range of technical and domain-specific skills. 54% of survey respondents said it was important that the necessary skills and expertise are available within the organization, or that they can be acquired through training, hiring and partnering.  In fact, many mid-tier companies bring in external expertise to implement AI and advanced analytics.

Almost half of the respondents identified failure to invest in change management as another risk. Analytics and AI projects can involve significant changes to processes. It’s important to proactively identify cultural and organizational success factors.  This includes getting executive buy-in, aligning analytics and AI strategy with business goals and communicating the value of analytics and AI projects to the rest of the organization, in order to build support and ensure successful adoption.

The stakes are high.  The mistakes leaders cited led to significant or total disruption of Advanced Analytics and AI strategies.  These challenges can delay realizing the business benefits, delay advantages against competitors or hamper defending against new entrants who use Advanced Analytics and AI.

What are the options when building a world class advanced analytics and AI capability in my organization?

Three paths that companies follow include:

1. Build the capability in-house

2. Buy third-party solutions

3. Partner with cloud consultants to accelerate customized advanced analytics/AI solutions

In summary, building Advanced Analytics/AI in-house offers greater control and the ability to tailor solutions to specific business needs, but it can be costly and time-consuming. Buying third-party solutions is quicker and less expensive, but it offers less control and limited ability to tailor solutions. Partnering with a cloud consultant can be a good middle ground as it provides a combination of in-house and third-party expertise and the ability to tailor solutions to specific business needs, but it is more expensive than buying pre-built solutions. Whichever path you choose, the benefits of advanced analytics and AI are well within your reach.

Take a look at the full whitepaper to learn more: Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Contact Ironside Group today to accelerate your Advanced Analytics and AI Strategies.

Data governance. This concept is emphasized differently among different stakeholders. IT representatives have always held a more restrictive and cautious approach towards enterprise data access, while the business users continue asking for more and more data to become available. 

As enterprise leaders continue to be energized by the transformational promise of the cloud, the need for a renewed strategy around who gets access to what data and how becomes obvious. In fact, 100 data and analytics leaders from middle-market companies reported in a survey that the eight mistakes most disruptive to their enterprise data strategy all have to do with data governance.

Data governance as a continual process

So what is data governance? Its central purpose is to improve trust in the data lifecycle. Trust in understanding where the data came from, how it’s been moved, if it’s been changed, and who can see it. How is this trust built? Through organizational policies and procedures that govern how data is managed at the enterprise.

What’s worrisome is that no phrase comes up more often with customers than, “I cannot trust the data.” The organization’s “currency” has not only become seemingless worthless, but also costly. Leaders and analysts alike cannot rely on what they’re seeing and decisions are being made blindly. Time is wasted arguing over which metrics are correct and the cycle is unending.

So, how can an organization avoid this total disruption of their cloud data strategy? Stop treating data governance as an end product – something to be completed – rather than a continual process. Data & Analytics leaders believe this is the biggest mistake enterprises make today – attempting to implement a whole data governance framework in one ‘bang bang’ and viewing it as a ‘one and done’ type of project.

Data management touches all aspects of an organization – which means a cultural shift is needed and this takes time to implement. Rolling out a comprehensive program should take a modular approach tactically and behaviorally.

Furthermore, effective data governance is never done. There will always be new data, new definitions, new technologies and new issues that arise over time. Without well-understood and trusted processes for addressing each of these areas, organizations run the risk of their program falling flat.

Building the right team is critical

While conversations about analytics tend to be saturated by technology, it is the people that make data governance work. A centralized team will define individual roles and responsibilities, ranging all the way from executive leadership to data stewardship. Business subject matter experts will work together with the data stewards to define key business processes and metrics, ensuring the consistency of data definitions to all areas of the organization. The data engineers and stewards are the key cogs to a governance operating model, which is why failing to establish these data owners in the business is considered a top risk to data strategy execution.

Technology powers successful implementation

While technology shouldn’t be the initial focus, leveraging modern governance tools will be essential in automating and scaling processes. 42% of the surveyed leaders were concerned with the successfulness of data governance policies if they’re introduced without the tools to implement them effectively

These tools can range from spreadsheets to data catalogs to enterprise governance SaaS platforms. Choosing the right technology is entirely dependent on where you are on the journey. Since a full data governance implementation is exhaustive, companies can quickly become overwhelmed by attempting to accomplish too much, too soon. This is why our recommendation is typically labeled “agile data governance.” Start with a focused set of initiatives (dictionary, quality, lineage) and then leverage technology to automate and monitor these procedures. This will enable teams to tackle other governance disciplines (master data management, metadata management) without being stretched too thin. It is the proper blending of people and technology that drives successful data governance.

To see the full graphic, download the whitepaper, Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Training and data literacy translate to business value

Once the key personnel, processes, and technology are in place within your data governance organization, scaling literacy and democratizing data is how you turn all this hard work into business value. Educating users on the available governance documentation and technology enables them to quickly get up to speed on why, where, and how to access data. These training sessions are technical but also help to establish a data culture. Leaders must evangelize the governance program and generate buy-in from the users by reinforcing why these best data practices are needed to ensure they aren’t circumvented moving forward. 

Consolidated and centralized access to data slows the value creation process. When too much of the analytics workload falls on IT resources, it restricts an organization from building competitive advantages effectively. Securely distributing and democratizing data to users across the business curates a smarter, faster organization. These technical and business users can spend more time in data analysis and less time dealing with data bottlenecks.

Typically, self-service and governance interests appear at odds with one another – one is an open policy, while the other is closed. But truthfully, the exact opposite is true – governance empowers self-service to be successful. With an enterprise more literate in data and educated on its lifecycle, the more it can confidently build value from it.

Protecting customer data and complying with regulations

Establishing rules and standards for data privacy, protection and security has always been a key tenet of the IT operating model. Global, state, and industry regulations have further tightened the data-handling standards that organizations must meet. And when we discuss data governance with our customers, the first thing they’re concerned about is remaining compliant and protecting all sensitive data.

Nonetheless, the last mistake highlighted by the surveyed leaders was overlooking data protection and privacy impact assessments. An assumption could be made that this would be considered a higher risk if leaders weren’t already confident in the data-compliant and protective culture that has existed for many years within their IT departments. But even if certain portions of the business are well versed in the protection of personally identifiable information, as data access is opened up to more users, more education will be required.

Viewing governance as a path to larger returns

As the appetite for data consumption increases, so should diligence in governance – it is needed now more than ever. Organizations should stop viewing governance initiatives as costs to the bottom line and start viewing them as mechanisms to generate larger returns from its most important asset (its data). In Breaking away: The secrets to scaling analytics, McKinsey identified what they defined as “breakaway companies” and found this group is, “…twice as likely to report strong data-governance practices.” 

The organizational excuses for deprioritizing data management over other analytics initiatives is becoming less acceptable and more risky. External sources can be a catalyst for mid-market companies, providing the leadership and experience required to build their internal governance functions they need. There are many avenues to getting started with data governance – organizations just need to take one.

To learn more, read the whitepaper: Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

With the maturing and ever increasing acceptance of the cloud across multiple industries and the data gravity gradually moving to the cloud, i.e. more data being generated in the cloud, we are seeing some interesting cloud-based data and analytics platforms offering unique capabilities. Some of these platforms could be disruptive to the established market leaders with their innovative thinking and ground up design that is “born in the cloud and for the cloud.”

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If your organization is seeking to better manage its information as a corporate asset that is to be valued and capitalized, you’re likely focused on implementing programs that will catalyze measurable business results from mountains of business information that may be the product of the last decade or more of digital transformation initiatives. Read more

Business intelligence has been around for a long time. From decision support systems in the 1960s through Ralph Kimball’s books on dimensional modeling in the 1980s, the core concepts of the discipline are decades old. As these concepts and the products built around them mature, more advanced techniques and technologies come to light that evolve and redefine what we thought we knew about the business intelligence space and business intelligence’s future. For instance, developments like the cloud, data visualization tools, and predictive analytics are changing the way businesses evaluate and make decisions from their data. Read more

On September 24th, Ironside hosted a webinar on Exploring Data Warehouse Strategies.   You will hear from our experts on different data warehouse strategies for traditional and emerging solutions.  Whether traditional, hybrid, or cloud-based, this webinar will give you insight on finding the best data warehouse option for your business. Read more

As we mentioned in a recent article, The Why, What, Who, and How of Successful Hadoop Deployment, there’s a lot you need to consider when implementing Hadoop to manage big data at your organization. Now we’ll build off that perspective and explore the data lake. Like any other new methodology just starting to gain ground in the information management space, there are a lot of assumptions about what data lakes can do and how they tie in with Hadoop-based infrastructures. In this article, we’ll discuss the most essential pieces of knowledge you need to wade into data lakes, dispel some of the rumors around them, and explain how they can fit into your information management ecosystem.

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In 2014, cloud data warehousing services led the information management category in increased adoption rate, jumping from 24% to 34% according to surveys by Information Week . For organizations challenged by data urgency needs that can be difficult to meet with traditional data warehouse infrastructures, cloud services offer an alternative that can provide value at the pace of business, often supplementing existing, on-premise data warehouses. With new technologies and advancements in the cloud data warehousing space, 2015 should prove to be an exciting year for those looking to build out or implement new cloud based DW programs. Whether you are in the midst of a cloud DW initiative, looking to start one soon, or just getting to know the technology – the five trends that we will discuss below are items you will want to keep in mind for the coming year. Read more

Netezza on cloudIronside is excited to announce a ground breaking new solution for organizations wanting to leverage their analytics workload-optimized PureData for Analytics / Netezza appliance in conjunction with rest of their virtualized private cloud analytics architecture. Ironside’s IBM PureData for Analytics (PDA) for Cloud offering solves all of the major hurdles for connecting your physical big data appliance with your virtual cloud infrastructure: management and communications bandwidth and latency. Ironside is now uniquely able to provide hosting and management of your appliance in conjunction with direct, private, and secure network connectivity back to IBM SoftLayer or other major cloud providers. This means you can host your entire analytics architecture in the cloud and still take advantage of the massive benefits that a physical appliance provides in this application. Read more

Tag Archive for: Cloud