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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
GSpeare@IronsideGroup.com
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
GSpeare@IronsideGroup.com
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
GSpeare@IronsideGroup.com
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

VendorAPIFunctionalities
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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Last week at Analytics University, IBM formally announced the release of the next major version of Cognos Analytics, v11.1.

IBM has hinted at the inclusion of “smarts” for “augmented analytics” and improvements in the usability of this new version over the past year. Our expectation was that these improvements would continue to “modernize” Cognos and help address some of the competitive pressures that organizations with legacy investments have been encountering in recent years. Read more

This is part five in our five part series on the essential capabilities of the competitive data-driven enterprise.

Over the last 20 years of doing business we have seen a number of different analytical data storage and query concepts fall in and out of favor. Throughout this time, a wave of digital transformation in business has dramatically increased the volume of collected data. Machine learning and other probabilistic methods benefit greatly from the law of large numbers so if by now it wasn’t already clear, all that talk about “big data” has really been about the analytics that it enables. As a result, today’s knowledge workers are predisposed to data hoarding, preferring to save everything including the data for which there are no known use cases, since its future value to the organization may still yet be discovered. Read more

This is part four in our five part series on the essential capabilities of the competitive data-driven enterprise.

5 capabilities_master data management

Businesses have been deploying enterprise data governance (defining what the data should be) and master data management (ensuring the data is as defined) programs for decades. Even if your company doesn’t have a formal master data management program by name, chances are good that they are doing some form of master data management in your data warehouse, CRM or ERP systems. As the trend towards decentralized data analysis continues to progress we see a few forces in play that make the case for incorporating a master data management capability into your organizational roadmap: Read more

This is part three in our five part series on the essential capabilities of the competitive data-driven enterprise.

Most business analysts will reach for their favorite data visualization tool when it comes time to perform driver and correlation analysis when in search of a cause. While this technology is essential for communicating with data, and excellent at identifying new opportunities (i.e. visualizing gaps or data non-relationships), it is limited in its ability to produce reliable, accurate and conclusive results. This is mostly due to our own human limitations when visually processing more than two dimensions of analysis at a time (e.g. revenue over time by product line). Read more