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Modern Analytics Framework Series – Part 4: The value of an iterative approach

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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  |  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.

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Modern Analytics Framework Series – Part 3: Creating Data as a Service (DaaS)

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In the same way that Software as a Service eliminates the need to install applications on your local network, Data as a Service lets you avoid storing and processing data on your network. Instead, you can leverage the power of cloud-based platforms that can handle high-speed data processing at scale. Combine that with the ready availability of low-cost cloud storage, and it’s easy to appreciate why so many organizations are turning to Data as a Service. 

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One key component of a modern analytics framework.

In Ironside’s Modern Analytics Framework, Data as a Service is one of 3 key components.

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How can Data as a Service serve your organization?

We know your time is valuable. So, let us speak to your specific needs. 

Schedule a time with Geoff Speare, our practice director.

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.

Elevate Your Data Science Initiatives with Data Enrichment

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In recent years, the field of data science has been advancing in leaps and bounds. In most enterprises, executives are aware that they need to be doing more with both internal data and external data by leveraging advanced analytics. They understand that machine learning and artificial intelligence will be rich sources for competitive advantage in the years ahead. The challenge for many is the question of exactly how to get started.

Many companies have begun to collect their data and ensure that it is stored where it can be put to use at some point in the future. That often includes text data, semi-structured and unstructured data including service tickets, user reviews, and social media posts, as well as more traditional sources like ERP transactional data. Simply by gathering, organizing, and ensuring that this information is preserved in such a way that it can be used later, these businesses are laying the foundation to gain future advantages from data analytics.

Many of them may already be well-positioned to gain significant business value from their existing data. Data enrichment makes that possible. It provides the “low hanging fruit” that produces powerful business insights, which, in turn, can drive immediate value.

According to a 2021 survey by Transforming Data With Intelligence (TWDI), approximately 30% of enterprises are already using external data. Just as many intend to begin using external data sometime within the next year. Trends surrounding geospatial data show a similar pattern. About one-third of companies are using location data in one way or another, and more than 25% of the remaining enterprises responding to the TWDI survey intend to start using geospatial within the next year.

Those trends illustrate a growing awareness that for companies seeking to elicit value from their corporate information, data enrichment provides a natural starting point. In a recent webinar co-sponsored by Precisely, experts from TDWI, Ironside, and Precisely discussed these trends, including how companies can get started leveraging data science more effectively to produce better business results.

This post was originally written and shared by our partner Precisely.

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Modern Analytics Framework Series – Part 2: Treating all data equally vs. a value-based approach

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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  |  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.