We are very happy to announce that the Pomerol Writeback Extension for Qlik Sense has been accredited into the Trusted Extension Developer (TED) program. The TED accreditation insures you a stable, supported, high quality solution.

See our TED page: https://developer.qlik.com/ted/5eb177184514100012efccc9

Qlik’s TED Program includes:

Extension Accreditation: Reduces risk of code failure on upgrade, ensures security protocol and data collection compliance, and allows accreditation on additional functionality such as responsiveness and mashup readiness.

Best Practices & Enablement: Detailed requirements documentation, test script creation guidance, monthly webinars, and open feedback channels like Slack.

Testing Tools: Qlik supported automated testing tools, and test script validation with Qlik Sense via a CLI to improve submission quality.

Assured Upgrades: TED members sign up with a commitment to provide on-going updates to their accredited extensions, as well as ensuring customers can receive support during their use.

All accredited extensions are listed on Qlik Market. Nevertheless, you will obtain the subscription and the support directly from Pomerol Partners.

What is the Pomerol Writeback Extension?

Our integration for Qlik Sense allows you to edit and enter data directly into your published applications. See the effects of your changes instantly. Reduce spreadsheet risk. Avoid task switching and complex data consolidations. Help your team by giving them a tool to increase productivity, and ensure your business makes the best decisions based on the right data.

The Writeback extension helps enterprise organizations save time, and money by:

• Improving collaboration across your teams.
• Improving the speed and efficiency of your data cycle.
• Improving the quality and integrity of your data.

See Pomerol’s Writeback extension in action!

1. Poorly Scoped/Designed

There’s more to life than time series. Time series forecasts are inherently bad at picking up the impacts of other dimensions. We get better results by clustering then running a time series. Or, better yet, run a 1X, 2X, 5X, 20X demand forecast. X can be hours, days, weeks, etc. The impact of holidays, product type, etc will be much more insightful; plus we’ll be able to forecast with less historical data.

What’s worse than picking the wrong type of model? Trying to do too much with a single model AND trying to do too much, period. Again, there is no one-size-fits-all approach. Trying to forecast apples and cats, you better have different models. Use cluster analysis to group like things first. Trying to forecast sales of every SKU? That’s also a waste of resources. Find the ones with the most impact first -revenue, losses, spoilage, inventory, turnover, etc. Ask yourself why you’re forecasting that product that counts for 0.001% of revenue.

2. Unrealistic Expectations

How accurate is your forecast now? Do you think you’re going to go from 80% accuracy to 99%? You’re probably not. Figure out what it really means to have a more accurate forecast. And figure out what a percentage increase in accuracy means to the company. It’s always good to be more accurate but some people think that if they don’t get 99% accuracy then it’s not worth it; that’s almost never true. Figure out what your target level of improvement is and shoot for that.

It is important to gauge the current state of your organization’s data capabilities before planning your machine learning project, too. Machine learning does not happen overnight and sometimes it is better to tackle data preparation and BI tasks before beginning any machine learning projects.

3. Bad Data/Lack of Data Prep

Every company has bad data. Every. Single. One. Fix it. Because the only thing worse than no data is bad data. Missing values. Dirty data. Inconsistent data. These are all problems that we can address but the wrong time to address these problems is when the model is being built. It ties back to expectations; if you give me dirty data then I will give you bad results. There are loads of tools we use to prep data – some you may have and some you may have never heard of. Tools such as Trifacta can be used to clean and transform data, as well as more traditional technologies such as SQL views and python can be used too.

4. Not Enough Data

Are you forecasting water sales but you don’t have temperature data? Are you forecasting capital equipment but you don’t have opportunity data? Are you forecasting preventative maintenance but you haven’t had any failures? We need to find a “signal” in the noise. If you can’t give us enough data there might not be a signal. This is why it’s so important to have an understanding of the business: to make sure a potential data set isn’t being overlooked.

Referring back to the notion of bad data, not including data because it is dirty is not a viable adjustment in many cases. Deleted data can have a drastic impact on a model and in many cases just needs some additional cleaning.

5. Too Much Success – Can’t Keep Up With Demand

So, you’ve created a model that very accurately predicts X. Now what? People keep asking you for more. And more. There’s no way to keep up with the requests from the “business” side of the house. Unless you have unlimited time, unlimited budget, or, the right tools. We’ve found that using a tool like DataRobot can help you build lots of models quickly and keep up with that demand. And you don’t need to hire more and more data scientists to do so.

6. Never Make It Into Production

If it’s not repeatable you won’t get results. One-off predictions will help you understand you business but they won’t help your people do their job better. What you need is a system that integrates your data, makes predictions, and, gets the information into the hands of the people that can take action on it. Imagine if Netflix emailed you once a month with suggested content rather than showing you on-screen what you might like. Also imagine if your doctor had to wait a week to tell you that you were about to have a heart attack. Designing the model and determining how it can go into near real-time production is how you truly build value.

7. Not Monitored

You’ve got the right model. Into the hand of the right people. At the right time. Now your accuracy drifts. What you thought was 92% accurate is now 80% accurate. How do you know? Without a system in place to tell you that things your model is drifting, there is no way for you to know that. Platforms exist to determine model accuracy faster than a data scientist could ever dream of.

Answer: First impressions last!

Heading off on a Tinder date, you’d be wise to style your hair, wear something smart, and think about what you want to say. Why? Because first impressions last. They can be make or break, and haunt you if you get it wrong. The same applies to building business intelligence dashboards – they need to look sharp, tell a good story, and be a joy to be around.

A typical BI application may consist of many dashboards or reports, but it’s the landing page where you set the scene, and send the user off on their journey. If you get this piece wrong your user risks being disorientated, and wandering without much direction.

Build a Qlik landing page that hits the spot. Here are the 4 key business questions I want answered on a BI landing page – along with the intended outcome.

#1.

Business Question: What happened? (focus your KPIs on the most recent/relevant period)

Intended Outcome: I know where we stand on key metrics that we use to asses the health of our business.

#2.

Business Question: How did we get here? (show trending up to today)

Intended Outcome: I know if we’re on the way up, the way down, or if this trend is cyclical/seasonal/repetitive etc.

#3.

Business Question: How are we performing against our benchmarks? (could be targets, budget, forecast, prior periods, industry benchmarks etc.)

Intended Outcome: I know if we’re we’re performing well or poorly, if we’re under or over based on the benchmark that matters (the most important/relevant benchmark will always differ from company to company, industry to industry, but remember ‘performance’ is always a relative measure).

#4.

Business Question: What are the key drivers of this trend/movement/performance (which product category, which sales territory, which week etc.)

Intended Outcome: I know what to go and investigate – I have some direction and purpose as I continue to navigate through the application.

If you can tackle these 4 business questions on your landing page your users will love you for it, and hey……you might even get a second date!

Tecumseh Poultry was founded in 1998 based on a simple goal, “to produce the best fresh chicken in the United States”, and that’s what they do! Over 20 years they have defined and then implemented methods which result in the highest quality finished products. In other words, they do things the right way. Not necessarily the easiest, fastest or most efficient way. Simply the right way.

So, what exactly does this mean? Proper treatment and care of their chickens and people, and the utmost care and discretion in how they process and package their product. Furthermore, they do all this while keeping a careful eye on the environment and on the sustainability and renewability of the resources they rely on.

Beyond being industry innovators in their day to day operations, they’re also 10 steps ahead when it comes to how they analyze their business and are visionary in how they use data to identify opportunity for improvement. In fact, Smart Chicken has more advanced predictive analytics than many investment banks we’ve worked with. Recognizing this vision early on, Pomerol was very excited to be given the opportunity to help Smart Chicken take their BI and analytics to the next level.

“Smart Chicken has more advanced predictive analytics than many investment banks”

No shortage of business challenges….

There are many challenges unique to producing a perishable fresh product. The shelf life is short, high wastage would make the business model unfeasible, and there’s no real opportunity for inventory. So, supply and demand need to be in sync, and this requires precision planning and a perfect understanding of your market. Supply is relatively fixed in the short term, but demand can fluctuate wildly depending on time of year, weather and promotional activities. Believe it or not, demand for white breast meat skyrockets for the short period of time that our new year’s resolutions are still intact, before settling back down in February. Try planning around that!

Beyond this, there are a raft of challenges unique to poultry production. Football season sees a huge uptick in wings consumption, but last time we checked, a chicken only has two wings, so there’s no ability to increase production of this single SKU. In another twist, Thanksgiving and Christmas is a time of year where we all get together and eat whole Turkey, but that sees the demand for whole chickens plummet for November and December. Planning around these erratic peaks and troughs causes plenty of pain, but together with the Smart Chicken team we’ve developed some novel ways of optimizing the planning process through a combination of live analytics in Qlik dashboards, and machine learning assisted forecasting with Data Robot.

Using Qlik to shed light on the business

The power of Qlik is leveraged for both its ETL capabilities and as a data visualization and reporting platform. Powerful associative models consolidate data from ERP, CRM, live production and planning tools giving managers an asset to empower decision making.

The customer demand forecasting process is a critical step in the chain to running a successful operation. Visualizing the forecast in Qlik, managers can quickly move from a brand level right down to individual SKUs. Different ordering patterns unique to geographies are easy to see, and the demand fluctuations driven by weather, events, promotions and holidays are all evident when trended or exposed through geographic maps.

Qlik provides a great view into the forecasting and sales data, but with such diverse ordering patterns there’s real opportunity in perfecting the forecasting process right down to the SKU and customer level. Smart Chicken and Pomerol enlisted the help of Data Robot to apply Automated Machine Learning technology to the demand forecasting process.

Taking forecasting to the next level with Data Robot and Automated Machine Learning

What really separates Smart Chicken apart from their competitors is how tight their forecasting and planning process is. Representatives in the field have their finger on the pulse by chatting to customers in supermarkets and they feed anecdotal information back into the planning team, while back at head office demand is forecast right down to the SKU and customer level, something most companies would not dare attempt.

Instead of applying a single forecasting algorithm across all customers and SKUs, Data Robot has enabled Smart Chicken to mathematically cluster and segment similar customers and products based on volume and ordering behaviors. Unique forecasting models are then auto-generated for each unique cluster.

Pulling from a library of hundreds of predictive models, Data Robot ranks its recommended models based on performance, error and speed, and then recommends a model to be put into production. Without an in-house data science team, Smart Chicken uses the power of automated machine learning and predictive analytics to further refine customer demand forecasting.

Beyond demand forecasting

While customer demand is a pivotal data point for Smart Chicken, there are other parts of the business that can also benefit from the power of Data Robot. Predicting the growth trajectory and final weight of chickens can inform the planning process of anticipated supply. In a sector where inventory is almost non-existent, having a keen eye on supply avoids surprises and lets the customer support team know exactly how much produce they have available to distribute. Having advance notice of any expected shortfalls lets customer service pro-actively manage expectations.

In another industry first, Smart Chicken have installed a full end-to-end ‘air chill’ system – the only one of its kind in the US. While the impact this has on product quality is beyond measure, it’s a tricky piece of equipment to run and presents a risk should it fail. Smart Chicken are looking to Data Robot for predictive maintenance. By analyzing the events leading up to previous parts failures, we can begin to predict when another failure is looking likely. IOT sensors constantly measure conveyor belt motor amperage, temperature and tension. The most minute change in tension may indicate a stressed or worn ball-bearing, or a slight increase in motor temperature might indicate an increased level of strain. Being able to pro-actively identify issues before they fail during production hours can have a massive impact on continuity of supply.

Looking to the future

As consumers place a higher importance on the quality and origin of their produce, the market for Smart Chicken’s products is on the rise. The game-changing innovator from small-town Nebraska is setting the bar for the larger producers. We’re excited to be on the analytics journey with them!

Visit the Smart Chicken website: https://www.smartchicken.com

Findorff is a $900M construction company based in Madison, WI. Their focus is conducting large-scale commercial and residential building projects. Given the change in the economic market, along with their steady growth, Findorff recognized the need to better leverage data and analytics to optimize job profitability, construction forecasting, labor productivity, and marketing & sales opportunities. Following an evaluation process, they decided to leverage the Analytics TO WIN® method to establish a data management and analytics strategy.

The Findorff team followed the Four-Step method and leveraged the pre-built assessments and templates to engage executive leadership, IT leadership and key functions of the business. This ensured the resulting data management and analytics strategy was developed collaboratively, with all three core business stakeholder groups having their needs addressed.

The Decision-Making & Data-Driven Culture assessment was invaluable. It generated much-needed awareness at the executive level about how decisions are made and about the maturity of their data-driven culture. It also provided a structured forum to confirm the corporate objectives, and raised awareness about their strategic data objectives (how is data provisioned, where it is stored, who has access, how it is used in decision-making). The session also helped identify what is needed to enable improvements in how they use data (data quality, availability, retention, etc.). The data and analytics team also took the same assessment and compared their results to the leadership results. This comparison exercise created a no-lose situation – i.e., if aligned, it would expedite next steps… if not, it would help bridge the gap between the data team and leadership to avoid delays. It also made sure that data initiatives were set to support the whole company, and both leadership and departmental leaders were aligned on priorities for maximum impact.

The Findorff team found the Analytics TO WIN® method to be very adaptable, and accommodated the company’s emerging strategic planning process (timing, personnel, input from leadership, IT and all business functions). It eliminated departmental subjectivity/bias via cross-functional consensus, established priorities and prerequisites, balanced load, and drove executive buy-in and approval. It increased confidence in the company’s ability to succeed and became the consistent reference point for measuring progress across all departments in the organization. The result of the Analytics TO WIN® method was a Strategy Matrix that clearly prioritized the most important data management and analytics initiatives. Each initiative was clearly aligned to their data-related strategic objectives, which were aligned to their overall corporate objectives. The data team was
also able to use the Strategy Matrix to prioritize some projects to achieve quick-wins to build early momentum. The Strategy Matrix was presented back to the leadership team on a single page that made it easy for the leadership to understand and approve the first set of key initiatives.

The data team at Findorff is now engaged in executing the prioritized initiatives from the Analytics TO WIN® Strategy Matrix and is already seeing success with the business

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