Tag Archive for: Business Analytics

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.

In today’s world, data is everything and the pressure to go digital  is constant.  Data-driven decision making is critical for organizations across industries to stay competitive. As a result, companies are aiming to achieve a maturity level that enables them to make decisions based on “collective facts” instead of facts  from siloed datasets.

Organizations need to enrich their existing data ecosystems with third-party datasets (weather, financial market, geospatial and others) as it has the potential of offering huge value via additional insights from the consolidated datasets. In addition, they also need to be able to analyze both operational and transactional data using the same analytics platforms. On top of that, the speed at which businesses would like to explore and leverage different data domains to gain business insights changes rapidly so it is important for your data analytics strategy to be nimble and agile to quickly adapt to changes.

Companies must review and revise their data management strategies to gain competitive advantage, maximize benefits and reduce technical debt.

Our research with 100 data and analytics leaders from various companies shows that 42% feel the hub-and-spoke model was ideal for their business, 38% feel the “decentralized” model suits their organization, and the rest only — 20% — believe that the “centralized” model is the best model for their needs.

Three Data Models and How They Differ

80% of leaders surveyed agree that a fully centralized data and analytics operating model is not ideal for their organization.

It’s becoming clear that leaders across industries are moving away from centralized models and that developing and implementing an effective hub-and-spoke model empowers business users, frees data specialists to focus on high-value initiatives, and gives management trustworthy metrics that can help drive impressive outcomes.

For a successful transition or adoption of the hub-and-spoke model, stakeholder alignment and approval is paramount. The model eventually needs to be accepted by the business functions who in turn will define the data domains and own the data products that will be delivered. Clearly defining the targeted outcomes will assist in getting the business buy-in that’s required, as modern data management strategies and architecture are no longer driven by IT alone.

There are seemingly endless considerations and many challenges on the path to  designing, building, implementing, and fostering widespread adoption of a hub-and-spoke data model. But you don’t need to go it alone. Find a partner who can meet you where you are and help get you to where you need to be. If you have specific questions about your data model or data management strategy, let’s make a time to talk

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

Success in the Cloud | Part 1 of 5

Cloud-based data analytics allows organizations to derive value from their data in ways that traditional on-premises solutions cannot. Organizations globally have already figured this out, and are now on journeys to realize the potential of analytics in the cloud. Most are about halfway through their cloud journey. What remains for most is getting past the “lift and shift” mentality that characterizes some cloud migrations, and educating higher-ups on the benefits cloud infrastructure can yield — ultimately driving buy-in.

While cloud migrations are well underway, few companies have completed the journey.

There are very few mid-market organizations utilizing cloud analytics to their potential. In a survey of 100 mid-market organizations, 66% are about halfway through their cloud transformation journeys; the number reporting fully modernized cloud-based data ecosystems was only 6%.

Lack of stakeholder alignment is a primary obstacle to successful transformation.

How can the 66% close the gap? Ultimately it is about understanding and being able to communicate the benefits of such a cloud transformation. What inhibits cloud adoption is not lack of value, it is misunderstanding.

The pandemic provided a live test case for the usefulness of cloud-enabled technology. Suddenly teams were separated by geography, unable to communicate in person. For the marketplace, this meant being able to access data remotely was no longer a nice-to-have, it was a legitimate competitive advantage that allowed prepared businesses to continue operating.

Beyond closing distance, organizations are discovering cloud enablement to be a useful lever of data governance. By its nature, data stored in the cloud is highly available and therefore easily accessible. Therefore, if a company has workers in Maryland, Alaska, and Hawaii, with the right preparation, access to sensitive data can still be distributed, controlled, and monitored as easily as on-premises infrastructure. Moreover, having one central source of truth can remedy the problem of siloed-off logic, “rogue spreadsheets,” and nebulous business logic.

Today these tools remain incredibly valuable. While the pandemic has abated some, the reality of remote teams remains, leaving cloud-enabled tools to bridge physical divides.

Common roadblocks mid-market companies encounter. 

Many organizations experience the same roadblocks on the road to fully realizing the value of the cloud. Among them are missed opportunities to revise data practices during implementation, reliance on outdated data management habits, and a non-uniform distribution of value-add within the organization itself. 

Evaluation of business process

Moving on-premises data to the cloud lends itself neatly to an evaluation of business processes. What is working well? What is causing problems? And what could cause problems down the road? To squarely miss gleaning the valuable insights from the answers, one might take a lift-and-shift approach to their cloud migration.

Lift and Shift

This approach misses a lot of opportunities. A lift-and-shift cloud migration (also called rehosting) is a 1:1 replication of on-premises data storage models, schemas, and methodologies on a cloud platform. When finishing a lift-and-shift, an organization will have the exact same business logic, databases, and workflows they had before, now on the cloud. This is complete with all the same advantages, bugs, and flaws as the prior system.

Data Management Practices

Outdated data management practices, migrating old and unused data, and a lack of education can hamstring a cloud migration. The lift-and-shift approach without evaluation of internal processes can lead to unneeded costs for the organization, and sap the sustainability from the new system. For example, is it worth moving a 6 GB Excel sheet last accessed in 2003 to the cloud? Or can that file be removed? These are the questions organizational introspection before migration can help to answer.

Non-Uniform Value Add Distribution

A key aspect of planning a successful cloud migration involves managing expectations. Making an organizational shift to cloud data storage won’t necessarily lead to uniform distribution of value-add across an entire organization. A data science team will find more value in a cloud-hosted data warehouse than an inside sales team. However, linking teams of various business functions to the same data warehouse facilitates synergy between them, creating value for all.

Executive Buy-In

The features of a cloud migration are important, but the biggest major obstacle to cloud adoption continues to be executive buy-in. Analytics leaders across industries consistently cite a lack of buy-in from leadership, specifically securing the budget for such a cloud transformation, as their biggest roadblock.

Communication about expectations, costs, and most importantly aligning cloud migrations with organizational goals (in both the short term and long term) is the most important tool for any analytics leader seeking to make the jump to the cloud.

The importance of cleaning house before you make the move.

Moving from on-premises to cloud-based data storage is not at all like flipping a switch. Instead, a cloud migration should be treated as an investment in existing business logic. There are limitations to hosting data on-premises that can’t be overcome due to the nature of the technology, and the way to maximize the existing business logic is by moving it to a new, more available platform.

A cloud migration is something that should only be done once. While a lift-and-shift will technically work, the organization misses the opportunity to improve processes, communication, and do some much-needed “housekeeping” of their data. This is hard work in the immediate term (during the transformation) but will quickly pay off in the short term, with long-term rewards that will compound over time. 

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

Your data needs are different from those of any other client we’ve worked with. Plus, they’re ever-changing. 

That’s why we’re fluid in our approach to creating your framework and why we ensure fluidity in the framework itself. 


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Whether your current investment in assessments, governance, and technology is heavy or light, we can meet you where you are, optimize what you have, and help you move confidently forward. 

These steps are all necessary, but don’t happen in a strict sequence. Each of them is an iterative process — taking small steps, looking at the results, then choosing the next improvement. You need to start with assessment and governance — unless you already have some progress in those areas. 

Analytics are constantly evolving, and the Modern Analytics Framework is designed to evolve more readily as users discover new insights, new data, and new value for existing data. There will be constant re-assessment of the desired future state, modifications to your data governance goals and policies, design of data zones, and implementation of analytics and automated data delivery. Making these changes small and manageable is a key goal of the Modern Analytics Framework.

Can we ask you a few questions?

The better we understand your current state, the better we can speak to your specific needs. 

If you’d like to gain some insight into how your organization can move most effectively toward a Modern Analytics Framework, please schedule a time with Geoff Speare, our practice director.

Geoff’s Calendar
O 781-652-5758  |  484-553-1814

Get our comprehensive guide.

Learn about our proven, streamlined approach to taking your current analytics framework from where it is to where it needs to be, for less cost and in less time than you might imagine.

Download the eBook now

Check out the rest of the series.

If you rely solely on a data warehouse as your repository,  you have to put all your data in the warehouse–regardless of how valuable it is. Updating a data warehouse is more costly. It also takes a lot of time and effort, which usually leads to long delays between requests being made and fulfilled. Analytics users may turn to other, less efficient means to get their work done.

If you rely solely on a data lake, you have the opposite problem: all the data is there, but it can be very hard to find and transform it into a format useful for analytics. The data lake drastically reduces the cost to ingest data, but does not address issues such as data quality, alignment with related data, and transformation into more valuable formats. High value data may reside here but not get used.

When you have a system of repositories with different levels of structure and analysis, and a value-based approach for assigning data to those repositories, you can invest more refinement and analytics resources in higher-value data.

Striking the right balance between refinement and analytics is key. Performing analytics on unrefined data is a more costly, time-consuming process. When you can identify value upfront, you can invest in refining your high-value data, making analytics a faster, more cost-efficient process. 

Our value-based approach can help deliver higher ROI from all your data.

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This value-based approach also helps your modern analytics framework better meet the needs of your knowledge workers. For example, analysts can jump into complex analysis, rightly assuming that high-value data is always up to date. In addition, automated value delivery automatically distributes high-value data in ways users can act on. 

Let’s invest in a conversation.

We want to hear about your current framework and your changing needs. 

Schedule a time with Geoff Speare, our practice director:

Geoff’s Calendar
O 781-652-5758  |  484-553-1814

Get our comprehensive guide.

Learn about our proven, streamlined approach to taking your current analytics framework from where it is to where it needs to be, for less cost and in less time than you might imagine.

Download the eBook now

Check out the rest of the series.

Today’s companies have no shortage of data. In fact, they have more than ever before. And they know that, hiding in that data, are insights for better business decisions and competitive superiority. 

But even with all the investments they’ve already made, in everything from data marts and warehouses to operational data stores, companies suspect the most valuable insights may remain hidden. And they’re often right. If your current analytics framework is no longer meeting your needs, the signs are all around you.

Top indicators that it’s time to evolve your current framework

  1. Frustrated users can’t find the data or insights they need to drive better decision making. 
  2. Users rely on self-created spreadsheets not accessible to others. 
  3. Analysts spend more time on manual updates than on actionable insights. 
  4. IT-owned analytic assets take too long to update, reducing their usefulness. 
  5. High cost to manage data curtails innovation. 
  6. Your framework is already overwhelmed by the data sources you have, leaving no room for new ones. 
  7. Value leakage — in the form of data you aren’t acting on — grows every day.

So, what’s stopping you?

In working with companies across virtually all major industries, we’ve encountered just about every obstacle that keeps companies trapped in a data analytics environment that no longer meets their needs. Here are the most common concerns we hear, and how Ironside helps to address them.

“We can’t walk away from the investment we’ve already made in our current framework.”

We get that. It’s why a core part of our approach involves meeting you where you are, and helping you move forward from there – not from the starting line. You keep what you have and invest in ways that will give you the greatest ROI based on your needs.

“We don’t have the budget for a big increase in analytics spending.”

We find that a lot of companies actually spend more than they need to by treating all their data equally. It all goes to the data warehouse, where processing costs are high. With our value-based approach, you could end up reducing your spend. 

“We don’t have the time or resources to take this on right now.”

We can function as an add-on to your existing team, so that they won’t be overwhelmed by even more to do. Plus, the new architecture could drastically reduce manual tasks that are taking up their time—freeing them up to focus more on generating game-changing insights.

Three framework components will help you reach new levels of data analytics.


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Let’s make time for a conversation.

We want to hear about your current framework and your changing needs. 

Schedule a time with Geoff Speare, our practice director:

Geoff’s Calendar
O 781-652-5758  |  M 484-553-1814

Get our comprehensive guide.

Learn about our proven, streamlined approach to taking your current analytics framework from where it is to where it needs to be, for less cost and in less time than you might imagine.

Download the eBook now

Check out the rest of the series.

The integration of BI solutions within business process applications or interfaces has become a modern standard. Over the past two decades, Business Intelligence has dramatically transformed how data could be used to drive business and how business processes can be optimized and automated by data. With ML and augmented analytics movement, BI applications are vital to every organization. Analytics embedding enables capabilities such as interactive dashboards, reporting, predictive analytics, AI processing and more within the touch of existing business applications. This differs from traditional standalone BI applications that put all the capabilities of business intelligence directly within the applications on which users have already relied. Now you may ask, when should I consider embedding to maximize my ROI?

Embedding Use Cases

Bar graph with upward trend     Business Process Applications

In this case, the integration of data & analytics is embedded into applications used by specific personas. For instance, embedding historical client information into a CSR application. One outcome will be improved decision-making based on readily available customer insights and higher levels of user adoption.

Shopping cart    Software / OEM Solutions

Digital transformation is all about software. Data visualization, forecasting and user interactions are must-have features of every application. Save the time you would spend coding. Embedding analytics in software not only saves cost greatly but also prominently enhances functionalities of software application.

Forest scene     Portals / Websites

Integration of data into your website or portal is another popular option. The benefits are obvious – information sharing provides your customers with valuable insights through a unified platform; you are able to go to market much faster since you are reaching customers directly. It helps your customers access the data they need to make decisions better, quicker and within their fingertips.

Embedding flow for embedding for your customers

Prepare for Embedding

Ready to get started? Let’s take a look at things to be considered. At a high level, the following areas to be carefully examined before design begins:

  • What are the embedding integration options? Especially with regards to security, how do you enable other application access to your secured BI assets? What are the options to manage authentication and authorization for thousands of users, both internally and externally?
  • Which functionalities will be open and accessible to BI embedding specifically? Typically not all UI functionalities are supported via embedding. Verify that critical functionalities are supported. Map your requirements to embedding functionalities and features.
  • Cloud vs On-premise hosting. Besides management and cost concerns, your organization may have cloud strategies and road-maps in place already. If that is the case, most likely no exception for BI application including embedding. Plus source data cloud modernization is another big driver to go with cloud. 
  • Cost – yes, no surprise there is cost associated with BI embedding. Each BI vendor may collect fees differently but legitimately you will need to pay BI embedding based on consumption pattern even when a single application user account is leveraged. Do the math so you know how much it will be on the bill. 

 Next let’s examine the tool differences. 

Embedding API by Leading BI Vendors

IBM CognosSDK – Java, .NetMashup Service (Restful)New JavaScript API for DashboardNew REST API   Full programming SDK is almost identical to UI functionalitiesSDK can execute or modify a reportMashup service is easy to web embedding, limited report output formats are supportedJavaScript API and extension for dashboard, display/editNew REST API for administration 
Power BIREST APIJavaScriptREST: Administration tasks, though clone, delete, update reports are supported tooJavaScript: provides bidirectional communication between reports and your application. Most embedding operations such as dynamic filtering, page navigation, show/hide objects 
TableauREST APIJavaScriptREST: manage and change Tableau Server resources programmaticallyJavaScript: provides bidirectional communication between reports and your application. Most embedding operations such as dynamic filtering, page navigation
AWS QuickSightSDK – Java, .Net, Python, C++, GO, PHP, Ruby, Command lineJavaScriptSDK  to run on server side to generate authorization code attached with dashboard urlJavaScript: parameters (dynamic filters), size, navigation

BI embedding opens another door to continue serving and expanding your business. It empowers business users to access data and execute perceptive analysis within the application they are familiar with. Major BI vendors have provided rich and easy to use API, the development effort is minimum, light and manageable while the return benefits are enormous. Have you decided to implement BI Embedding yet? Please feel free to contact Ironside’s seasoned BI embedding experts to ask any questions you may have. We build unique solutions to fit distinctive requests, so no two projects are the same, but our approach is always the same and we are here to help.

In previous releases of Cognos Analytics, we have seen a trend of integrating many of the features of metadata modeling in Framework Manager into the Cognos Analytics interface. This trend is continuing with new or improved modeling capabilities being incorporated into Cognos Analytics 11.1 Data Modules.

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2019 is the year that data science, machine learning and artificial intelligence for business will become ubiquitous. Most organizations large and small, across all industries, have recognized the benefits and competitive advantage that these capabilities bring to bear. If you have not already begun the journey, chances are this will be the year you begin to develop this competency. Whether you’re about to take your first step, you’re a team of one looking to scale, or even a more mature organization that is always seeking self-improvement, consider the following traits to maximize your chances of success with data science.

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