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How Can Companies Get The Most Value Out Of Their Data

Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets.

Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and this article lists five things all companies must do to accomplish this endeavor.

A little bit more about me before I dive in:

Over the last 8+ years, the breadth of organizations I’ve been fortunate enough to help as a Data Consultant spans multiple industries such as: Retail, Financial Services, Legal, Pharmaceutical, Hospitality, Food and Beverage, and Professional Services Firms.

The depth of technical skills and tool I’ve gotten a chance to implement spans the end to end data value chain which is:

  1. Data acquisition, ingestion & integration (Informatica, Talend, AWS Redshift, S3, PostgreSQL, Hive, Hadoop, etc.),
  2. Data visualization tools (Tableau, Qlik, PowerBI, etc.),
  3. Leading EPM tools (Hyperion, Anaplan, etc.) and
  4. Emerging data-driven SaaS platforms such as Quorso.

Without further ado, here are my best practices on how organisations in all industry verticals can maximize the value of their financial and operational data sets.

  1. Consolidation.

Common data hierarchy across the enterprise.

Data & Insights are no longer the cherries on top. They are the base upon which all successful organizations are built.

Building a common data hierarchy requires two bits of work:

  1. Developing a common data structure in which all enterprise data should reside.
  2. Developing data pipelines that continuously take data from all relevant data sources and transform them into the common data structure.

For example, if an avocado in location A is stored as “AVC” and in location B is stored as “AV”, the end result of point 1 and 2 listed above would be a hierarchy that shows:

Food > Plant-Based > Avocados > price, volume, & sales.

Having a continuous data pipeline that structures data into this type of format daily, weekly, or monthly will allow anyone to generate and action insights generated from data sets without having to do any custom data-wrangling each time they need to ask questions to their data sets.

  1. Insights > Charts.

A focus on creating insights rather than fancy charts and visuals.

Business Intelligence, Enterprise Performance Management, and other reporting suites offer the promise of insights via visuals and dashboard but that’s it.

Visualising data is a good step forward from manually cyphering through large amounts of data sets using complex queries, but dashboards and visuals are not enough.

Rather than focusing how best to visualise a trend, companies should focus on how best to generate relevant insights using their data sets. Visuals may assist in insight generation from time to time but the goal should be to generate insights rather than a fancy chart here and there.

For example, visualising the sales of avocados across 100 grocery stores using a line chart and time series will show us how avocadoes are trending up or down. This is a nice bit of information but it doesn’t really tell us anything insightful. Overlaying last year’s dataset for the same time period is another nice bit of information but lacks anything insightful.

Instead, clustering comparable grocery stores and then analysing the sales of avocados against the average within a comparable peer set is much more insightful.

It’s an insight, not just a chart. ­

It shows a potential problem that we know must have either a solution or a valid explanation.

That is, if other comparable grocery stores are able to maintain a certain level of avocado sales then so can one that is underperforming by learning from the stores performing above average.

For example, if 3 stores out of a comparable peer set of 10 is driving avocado sales through local campaigns, perhaps the other 7 should adopt the same campaign.

On the other hand, if there are factors outside the store’s control (natural disaster at avocado farms), these should be noted to ensure the organisation is capturing these explanations.

  1. Track Actions.

Track every action taken on each insight (both good and bad).

Each time an operational action is taken on each insight, it should be tracked.

This is primarily where BI and reporting tools start to fall short.

No BI tool has robust write-back functionality that allows for operational users to track actions that they’re taking and how their actions impact the data.

For example, just having a fancy chart that shows avocados sale trend lines won’t allow us to see how sales have gone up or down since we started a local campaign. Indirectly, we can do some manual analysis on standardised charts to find this information but a fancy front end on its own is not enough to track 1,000s of operational actions being taken by the front line on the insights that the data is generating.

Enterprise-level communication tools (Slack, Teams, Email, etc.) allow users to indirectly share actions but these actions don’t tie back to the data where the insights are coming from. Even enterprise giants like Microsoft have only gone as far as embedding dashboards into communication tools but the back ends are not linked and tracking actions is still very much an ad-hoc exercise.

  1. Measuring the impact of each action.

How does each action affect EBIT and your strategic Key Performance Indicators (KPIs).

Simply measuring performance before and after a certain date where a plan or campaign was launched to increase sales, reduce cost, or optimise labor is not enough.

Tracking how actions affect EBIT (profit), operational metrics, and strategic K.P.I.s allows us to truly use insights to improve business performance.

For example, it’s easy to indirectly look at basic trend lines to see if a campaign to drive avocado sales is increasing or decreasing sales. However, this isn’t the most useful way of measuring success.

What’s harder and more worthwhile is to track the gap to median improvement among comparable grocery stores to truly see if the campaign is working as well as track how this action is effecting the organisation’s KPIs and operational metrics. That is, avocado sales going up but a poorly positioned display or sign causing higher wait times at checkout and thus driving customer satisfaction down is as important to measure as the sales number.

  1. Sharing relevant peer actions.

Scale successful actions across the enterprise.

Operational problem solving is a human exercise. There’s lots of technology than can augment good decision making but this will always be an exercise where human ingenuity is at the core.

People too often still solve problems in silos, especially in large multi-site distributed businesses. This problem becomes even more apparent when large multi-site businesses operate in a franchise model.

For example, if Jane Doe operates grocery store X on Main Street and has been crushing performance in Food > Plant-Based > Avocados due to well-timed promotions, consolidating avocado supplier spend, and training her direct reports on how to upsell avocados — this great but Mary Doe 1 mile down the street that operates a very similar grocery store operated by a different franchisee may never be able to learn from Superstar Jane Doe.

This is important because not scaling the most successful actions across the organisation limits the organisation's full financial and operational potential.

How can organisations accomplish steps 1–5.

From my experience, there’s no shortage of tools that can help any organisation accomplish each individual step listed above.

However, there’s only one tool I’ve come across thus far in my career that helps organisations accomplish all five of the above points in one single tool — Quroso.

Quorso was founded by former McKinsey & Co. Partners after they came to the realisation that there are roughly 20–30 million people that control 60–70% of the global economy, specifically as it relates to profit. These 20–30 million people are folks that make decisions daily, weekly, monthly, quarterly, etc. that directly affect their organization’s P&L. The number of people and the percent of the global economy this will affect is only going to go up as more and more people enter the workforce.

At Quorso, we’ve built the world’s first intelligent platform that allows organizations of any kind to surface relevant insights, take & track action on each relevant insight, measure EBIT, KPI, and Op Metric performance for each action, and share best practices across the organisation when actions are successful.

Since launching our product, the industries that have taken akin to our creation have been Retail, Transportation & Logistics, Hospitality, and Food & Beverage.

I joined Quorso as their first Data Engineer and our Data Acquisition & Ingestion engine is capable of working with any type of data source. We then create a common data hierarchy and continuously load data into the platform based on the appropriate time series cadence.

The way we expose relevant opportunities is by using a recommendation algorithm analogous to Netflix, Spotify, or iTunes to ensure the opportunities are relevant to the user based on prior experience, the area of the P&L they control, strategic company initiatives, etc.

We only show controllable opportunities — this is what marketing experts call “actionable insights”.

Every action in Quorso is tracked and the app is smart enough to share relevant peer actions to the right people to ensure problems are not being solved in silos, and thus, organisations that use Quorso perform up to their full financial and operational potential.

Originally posted here

 

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Tags: business, data, enterprise, intelligence, management, performance, saas, science

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