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“Dashboards are sexy, dashboards sell”... But how can dashboards deliver success?

Rohit Kanwara

Posted by Rohit Kanwara on 09 October 2020

Dashboards are sexy and sell but how can dashboards deliver success?

Most analytics initiatives start with a single problem area and a user who is keen to try something new to achieve a level of insight from the organization’s data. Whether the initiative begins because individuals are spending too much time sorting through data—or the organization is taking a “me too” approach and simply wants to keep up with their peers - analytics has been and will continue to be front-of-mind for most technical and business users.
Today, visualization solutions have become a commodity. Dashboards are sexy, dashboards sell. But, in order to deliver these dashboards in a way that allows a user accessing multiple systems to see success, there must be a plan to consolidate data and achieve the appropriate insights for the business.

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Microsoft advocates the use of SQL Server Analysis Services (SSAS) for aggregating data and delivery to self-service tools, but many businesses either don’t have the skills, don’t have the time, or simply don’t have the resources to connect and deliver analytics across multiple systems, either in the cloud or on-premises. They end up skipping this data aggregation phase and a data model architecture as it looks like a bowl of spaghetti.

Our customers see success because they have a real business need that must be fulfilled. The vast majority successfully complete at least the first two stages of what I call the ‘Crawl, Walk and Run’ phases of an analytics strategy.

I’ve written previously about the difficulties businesses face when considering big data. Frankly, this is predominantly due to internal constraints and the need to leverage the 50% of structured data that is currently ignored within most businesses. To form an analytics roadmap, we must review from a high-level the values and key areas of focus for greatest return, while focusing on a data management strategy that promotes agility.

Thus, the ‘Crawl and Walk’ phases of an analytics strategy are where we focus on:

Phase 1: ‘Crawl’—tackle a key business challenge

When your organization is struggling to deliver basic financial and operational data, focus on one key area allows for the introduction of a solution set that can be managed and deployed quickly. This allows the organization to structure one or multiple data sets to deliver high-level self-service analytics and a fast, measurable return on investment.

Once these initial needs have been met, the return on investment will have been realized, but the full potential value of business intelligence may still be out-of-reach. Focus on the next initiative will result in long-term success.

(You might also be interested in "4 Ways to Confront Data Quality with Business Intelligence")


Phase 2: ‘Walk’—introduce KPI's and dashboards

It’s now time to move beyond reports, move past the initial challenge, or manual processes that have been resolved. The baseline data management iteration is in place and—perhaps with an initial focus on the accounting department—the financial period close is now 40% faster.

Now it becomes time to develop visualizations at an executive level that allow for management by exception and the delivery of that exponential value business sought when purchasing BI solutions.

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Perhaps a focus is needed on Supply Chain and a dashboard with data from demand planning and ERP applications, coupled with statistical data points related to CRM forecast accuracy?  In my experience, successful execution of this type of initiative can lead to substantial gains in efficiency, and returns of 10X or more attributable to data management and analytics.

However, Phase 2 is also where a BI initiative can come off the rails, as the business case might be rather more nebulous, the overall objective less clear, and the time available for those involved is stretched. (It’s difficult to devote to something that has always been based on instinct.)

Walking down the path of this analytics journey takes investment: time, resources, and capital. But it’s well worth it, as the value is clear when year-on-year revenue gains and cost savings across inventory, marketing and sales begin to materialize. The return on the ‘Crawl’ phase is typically weeks or months. That’s the easy part. The return on the ‘Walk’ phase requires months and dedication. But, once achieved, the true fun (read: return to the business!) will begin.

(You might also want to check out "Sales & Marketing Reporting: Top Analytics to Track in Power BI & Tableau")

Phase 3: ‘Run’—leverage unstructured data, introduce data scientists and a team of personnel to drive gains from data big and small

For those dedicated businesses who made it through the ‘Crawl’ and ‘Walk’ phases of their analytics journey, they can start to take advantage of the essence of Business Intelligence. It is no longer about incremental gains in automation or the delivery of baseline reporting. The focus is now on consumer sentiment through unstructured data, machine learning and artificial intelligence for exponential marketing and sales growth, combined with resources dedicated to delivering value in every area.

Unfortunately, most mid-market organizations begin to become unwound in their analytics journey between the ‘Crawl’ and ‘Walk’ phases. The expense associated with the delivery of the ‘Walk’ phase is tangible, and those who have not planned for this journey may feel they’ve exceeded their comfort zone. This focus and planning can—and will—drive exponential return when architected correctly.

The view that software will be the ‘silver bullet’ for success is removed as the business’ culture transforms to being data-driven.

Business Intelligence is not about headcount reduction (although some will have you believe this is the natural result of automation), it is about removing instinct-driven decisions and leveraging data to drive returns to the business.

How much would a 5% improvement in inventory turnover impact your business?  How much would a 5% increase in customer retention effect your bottom line?  How would a 20% improvement in employee attrition rates improve customer retention and inventory turnover?

These areas can be focal points of the analytics journey, but for those who skip the ‘Crawl’ phase or do not plan through—at a minimum—the ‘Crawl’ and ‘Walk’ phases of the journey, be warned: your reduced data agility may not survive the next industry or economic shift. A defined plan must be in place, and clear objectives delivered, to achieve success in the commoditized analytics world.

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About the Author:

Rohit Kanwara
Rohit Kanwara
Rohit is the EMEA Regional Manager for ZAP with over 10 years of B2B software sales experience in selling ERP products like Sage X3 and SAP Business One. Rohit is passionate about helping businesses improve through technology and innovation by understanding their challenges and offering tangible solutions.
View my social profiles: LinkedIn |

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