I wrote this article as part of a series published on the Mercury Media Technology GmbH blog.

We have developed a guide for you to facilitate the planning of this high-potential transformation for your business. It addresses the most pressing questions that managers will have to cover to set out on this exciting journey:

• What are data-driven solutions?
• Why invest in data-driven solutions?
• When to invest in data-driven solutions?
• How to invest in data-driven solutions?
• Which steps are needed to get started with the transition?
• How to estimate the costs?

What are data-driven solutions?

Technical solutions which enable the handling and leveraging of data, aiming to improve business outcomes through transparency, cost-saving, and internal efficiency. Datasets could originate from various sources:

• Internal: Supply Chain Management, Customer Service, Advertising, and so on
• External: market data, competitors, legal environment, and other forces which can influence the company’s business.

The resulting outcomes can include production costs, sales, loyalty, retention, margin, average basket price, ROI… These solutions are named Data Science, Business Intelligence, Data Analytics and so forth. Their common point is that they are objective-based and drive tangible improvements enabled through data.

Why invest in data-driven solutions?

Because no business is capable of analyzing its data manually anymore. It is not only about collecting, organizing and presenting data but also about making sense of it and leveraging the analysis to reach the company’s goals.

Some companies may have implemented super powerful excel macros to convert part of their data into insights. But not to the fullest. Why?

• Because The World Economic Forum estimates that by 2025, 463 exabytes of data will be created each day globally – that’s over 210 million DVDs per day! That means a LOT of data!
• Because more data is being created while number crunching
• Because number crunching is time-consuming, so decision-makers have to wait
• Because manual solutions cannot encompass the full spectrum of data
• Because companies cannot resort to a truncated view of their businesses

The pandemic exposed companies’ weaknesses in terms of anticipation and adaptability. The ones that have digitized show better ability to navigate and act in times of crisis. Do I really need to detail this further?

When to invest in data-driven solutions?

If the above “why’s” are not relevant, Companies may start a transition to the data-driven model for the following purposes:

• Efficiency purposes: data-driven solutions provide analytical support for continuous improvement to drive a business towards its goals
• Transparency and quality control: automation and centralized reporting help deter fraud and reduce human error which could generate great savings
• Agility: a single source of truth at any given time reinforces the company’s capacity to decide quickly based on a clear common vision.

If you want to save on resources and energy as well as avoid unnecessary stress – then it’s about time to invest in data-driven solutions.

How to invest in data-driven solutions?

Transitioning to data-driven business decisions needs some preparation to guarantee success as well as quick internal take-up of the solution after the team onboarding (just like in any well-managed project, as my expert project management team member would confirm).

• “I need a dashboard to keep an eye on everything”,
• “Can you show me the performance results of the top 20 markets first thing tomorrow?”
• “Can you send me this client’s sales volumes and commitments at the regional level over the last five years?”

Typical requests that we hear daily at work, requiring hours of manual work, which involves multiple contacts within the company, even on a global scale. In order to keep pace, the company needs a robust communication network.

Acquiring a BI SaaS license, purchasing a visualization suite or running a data analytics project will give you a fish, but it makes more sense to learn how to fish yourself: only a long-term solution will bring sustainable results.

5 Pillars to get started with transitioning

The transition to data-driven decision-making requires the following: Involving all management levels will help build the right momentum to make sure the project comes to fruition. To encourage the evolution of the decision-making process, the actual decision-makers need to feel comfortable with what is being brought to life (sounds like common sense, doesn’t it?)

1. Long-term objectives:

For this, it would have to start with a clear definition of the objectives to be achieved. It often requires an assessment of each department’s needs and future developments to visualize holistically the potential economy of scale and data synergies to be leveraged. 

While the list of objectives can be endless, the implementation timing and budget are always limited, but we will come to that.

2. Leaders of the data-driven transition:

How many people are going to be affected in their daily job? Is data-driven model implementation an IT project? Or a Finance one? Who is leading the transformation of the company?

This comes as a hurdle for many businesses: limiting the process to one department creates silos while involving everyone hinders decision-making. This is where external partners prove useful to handle multidisciplinary projects involving all stakeholders while keeping the focus on implementation timings and overall goals.

3. Enablement of the data-driven transition:

Do we contract an external partner or build an in-house infrastructure? Do we buy the servers or host them in the cloud? What are the requirements and restrictions of both alternatives?

It entails building a vision for long-term usage because it influences the scalability and agility that the system will require over time. This is an extremely important part of the process as it determines the financial scope of the project.

We will dedicate an article to the topic of structure and – we will link it to this section.

4. Timing:

There are few limits to what programming and machine learning can do – except being humans: these are rooted in urgency as we explained earlier in “Why focus on code quality in media and marketing software development?” article.

It’s essential to find the balance between complexity, scalability, and speed because needs will become more sophisticated as the data maturity of the organization grows.

There is no right or wrong answer to the debate whether to buy off-the-shelf or get a development team to build your proprietary solutions internally, it all depends on the objectives and ambitions of the business with respect to data. But you know: if you want to master driving a Ferrari at Le Mans, you may start training with a Skoda in a parking lot, but it is no use in the long run.

5. Roll-out plan:

Launching a new process across the entire organization and making everyone adapt overnight is truly a challenge – but it is not impossible. It requires good coordination and strong will as well as organized prep time: internal communication, training, test sessions and planned release to get the teams up to the challenge.

Launching sequentially (per region, per department, etc.) would generally take the same time, but organized into a series of smaller launch phases with shorter cycles. 

“Designing a roll-out plan including training, assessment, and iteration phase at each step of the process will secure higher efficiency and company take-up.”

Aiko Müller
Director Implementation & Support, MMT

How to estimate the cost?

As a starting point: the basic calculation deducts the resource savings (man-hours saved on reporting/data gathering or number crunching) from the solution costs (which would usually be a SaaS license).

The costs of designing, building and implementing a more sophisticated data-driven solution have to be visualized in the long run as benefits and needs will evolve along with data maturity.

The benefits of transitioning to data-driven processes are ranging from faster reporting, more transparent decision making and information sharing, to better cost control and resource allocation. It can boost the agility of future business decision-making but it will not happen overnight.

This could be enough to start exploring investments in many basic data-driven solutions but it would be shortsighted to limit the calculation to this, as the cost of data-driven decision-making is 4-fold – all offering the choice between internal and external resources:

• Cost of data: the gathering (data connexion and provision) and storing it (servers or cloud infrastructure and its maintenance)
• Cost of resources: IT (for servers), data engineering (for cloud computing and provisioning), data analysts (for modelling and visualization), business intelligence (for insights)
• Cost of implementation: setting up the team, training and follow-ups, support and knowledge base management => without forgetting the time that teams need to invest into the training and usage learning
• Cost of change management support: to guarantee successful implementation, it is important to monitor the changes and push innovations forward. Such investments can be an absolute waste of money when no assistance is provided to the people who have to make their practices evolve. Change management is what would actually achieve data maturity across the organization

Then the nature of savings:

• Resources savings: FTE spend on collecting, preparing and visualizing data that now can be automated; reporting preparation (internally or across suppliers)
• Company savings: that real-time decision making has allowed
• Efficiencies: in the company operations and supply-chain that data maturity offers.

Conclusion

As we love to call it, the disruptive aspect of data in our world is so mind-blowing that companies have difficulty comprehending it, sometimes even reluctant to accept it. The reason often originates from the risk-averse leaders who foresee disruption as a potential negative impact on the business in the short run and would rather let the next senior team take the plunge, and keep their own legacy protected.

Sometimes disruption is just too much, and transition offers a “safer” – more acceptable – perspective to those risk-averse managers. It creates the potential for learning and growing with the new approach given the necessary time for the team to reach mastery and become more comfortable with the new practices. That is why “change” is to be managed and not enforced, it brings stronger business benefits over time.

I wrote this post for MMT

Transitioning to data-driven marketing, which has become a must-do for business performance in the last 5 years, is not uniquely a mindset to adopt.

Using efficient data for decision making is not about building data-heavy graphs in business presentations to help you determine your next business steps. You need an infrastructure setup to collect those data sets but also to compile and often visualize the data in dashboards so that you can leverage it. You need to be able to obtain, organize and read the relevant data so that you can make smarter business decisions.

Manual preparation of reports and data can be time consuming and resource heavy, which can lead to duplication and errors that can easily be overcome by automating fetching flows and updates. 

Table of content:

• Is there a specific issue to data collection in the advertising industry?
• How do you connect data flows?
• Next steps

Is there a specific issue to data collection in the advertising industry?

From the beginning of the media digitalization era, each individual publisher and service provider has done its best to keep up with technological developments on the market. However, the industry at large has not managed to set standard processes for buying and delivering media (some countries have independently created a standardized approach, but this is not the norm). The absence of standards makes it challenging to find fast and unique data flow connection solutions that work for everyone. Marketing teams, wishing to get a clear view of their media activities, have to individually connect the data from the necessary platforms like social media, programmatic, DMPs, publishers, TV booking systems, etc.

How do you connect data flows?

Solution 1: Third-party provider

Some companies have made it their mission to help marketing teams easily get these connections by acting as a third-party service provider. They have developed adapted solutions that deliver formatted data to their client’s warehouse (or theirs if their client does not have one) where they organize the data. This has made connecting data provision fast and resulting in quick launch times for data-driven projects, offering speed and simplicity at a cost. If businesses do not have the team or the experience needed to build the data infrastructure necessary to run their own solution, these providers can be extremely helpful. 

This connection still needs to be leveraged through an additional visualization solution, as the connection system does not include a direct interface to “read” what it has extracted. What’s more, the meta-connectors do not offer ad-hoc offers, and small businesses end up paying for a more complex service that they do not need.

This also deprives marketers of transparency regarding data delivery and control as it means being dependent on the provider’s ability to offer the required connection and regular updates no matter what happens and because update issues that may arise are not always anticipated (e.g. the database does not update properly the day you have the largest budget presentation of the year).

Positive: Data connection is made fast and easy to a very large number of partners. API speaks directly to your applications.

Negative: This can lead to a lack of transparency and flexibility on a couple different fronts: data-availability (when new data is ready to be consumed) & troubleshooting. No savings possible: You pay for a Ferrari when you need a Fiat.

Solution 2: API connection

Modern platforms mostly offer API access to catch this data – API being the layer of software which allows communication with another application – to allow data extraction at regular times and maintain an updated database. Some make it possible to generate data ingestion as well, which makes it possible to create a centralized self-service solution that can give commands as well as receive information.

But again, API is built into the code of an application and does not follow a particular standard in the marketing industry, which forces businesses to address these connections one by one in order to obtain their own data. Connecting to some of these APIs is extremely challenging. Only veteran engineers can code a working connection and manage to fetch the relevant data.

On the positive side, APIs offer transparency and control over the data flow and engineers can quickly resolve issues and troubleshoot connections if anything happens.

Positive: Provides direct contact to your dataflow from a particular platform with control and transparency.

Negative: Requires qualified engineers to connect and maintain your connections to your application. It embeds the API in your application => which means that your application has an API too.

Solution 3: RPA

Robotic process automation (RPA) is a surface level automation software that can be used to allow applications to talk to each other.

For those who haven’t yet incorporated a dedicated protocol for data sharing, human actions can be “mimicked” with a piece of program that retrieves the necessary information. This is called robotic process automation, or RPA, and once coded it can perform tasks the human hand would perform otherwise, like basic chatbot solutions or a report generator on a platform UI. It is a simple piece of automation, not an AI, and performs repetitive tasks like copy and paste, logging to applications, scrapping data and making a calculator very well. It is fast to implement and does not affect the structure of any of your software applications, which keeps it from disrupting everything that is already in place. However, it can easily crash or underperform of there is a disruption of the environment. A basic RPA can only deal with the keyword it knows and will not identify spelling mistakes. However, it would be a perfect way to update a database when using forms to fill dropdown menus and ticked boxes.

Positive: RPA is a good solution for fetching data when no API is available. It is cost-efficient and can be an effective way to replace repetitive human tasks, thereby reducing potential human errors.

Negative: RPA does not determine whether the task has been well-executed. It can only rely on preformatted information to guarantee quality results.

Next steps

At MMT, we have studied the connectors available on the market, and our team is able to interact with API as well as write RPA. We adapt our client offers to the most relevant solutions for them, their business, their needs, both long term and short term. Get in touch with us for more details.

—

Sources: 

• Jake, the most patient member of our development team
• https://en.wikipedia.org/wiki/API
• https://rapidapi.com/blog/how-to-use-an-api/
• https://medium.com/@TebbaVonMathenstien/what-is-an-api-and-why-should-i-use-one-863c3365726b
• https://www.ibm.com/uk-en/automation/rpa

I wrote this post on MMT

Walled gardens offer an amazing platform to get brand presence quickly to the market, targeting core audiences for what seems a fraction of the budget. They have become into an essential tool for building awareness to the point of overspending other digital channels and most other media channels in many countries in the course of the past 10 years. But what then? Why scale out? Read on to find out the why and the how-to kick-start your company’s ‘scaling out’ for success.

Questions this article answers:

• What are walled gardens?
• What are the benefits of using walled gardens?
• What are the problems with walled garden advertising?
• Why should brands that wish to grow not to limit themselves to walled gardens?
• Where are the media opportunities outside of walled gardens?

What are walled gardens?

Definition Walled gardens Walled gardens are closed platforms requiring a login to access data. They are able to track every single detail of user activity within their universe without having to share details with users or advertisers. This is the first-party universe: information on users is constantly collected, consent is given by subscribing and accepting of terms of use.

The different walled garden platforms offer 24/7 content service skewed towards the users’ assumed desires and served through a platform designed to maximize time spent. The most famous ones are:

• Facebook groups (Facebook, Instagram)
• Google (Youtube)
• Amazon (marketplace, Prime, Music)
• Apple (TV, Music and Apps)
• Tencent (QQ, Qzone, Weibo)
• Twitter
• Linkedin
• Twitch
• TikTok
• Pinterest
• Snapchat

Facebook and Google account for over 50% of digital advertising spend worldwide.

What are the benefits of using walled garden advertising?

Thanks to the close universe they created, walled gardens provide a precise set of targeting opportunities not only based on location and time but also on behaviors and tastes. They can also guarantee deduplicated cross-device advertising, as they rely on a login solution to address users across all their connections.

Walled gardens provide a controlled environment for advertisers, giving confidence that is safe from conflict, violence and horror stories. They also highly qualify the indicated target audiences and geographical areas when configured properly.

They are still the most mature alternative to cookieless advertising of those solutions expected to hit the market in the next 12 months or so (with changes to Safari ITP, Firefox ETP and Google Chrome to come in 2022) 

What are the problems with walled gardens advertising?

Walled gardens do not share data with their customers. They report aggregate information on campaign engagement and performance without being transparent in regard to the campaign’s global advertising visibility, which prevents advertisers from acquiring deeper knowledge about what works and leveraging information to help them save on spending via the details on algorithm selection.

That means that any effort to learn from walled garden activities and mechanics cannot be applied to other solutions, as usage differs on top of the provided campaign results.

Time spent on walled gardens is not aligned with advertising spending. Walled gardens represent less than 40% of the time spent online in the US, according to Openx, and 40% in the UK, according to WARC. Statista reports walled gardens at 27% of daily social media usage in Germany in 2019. Despite this data, Facebook, Google and Amazon attracted 67.9% of ad revenues in the US in 2019, according to eMarketer, these platforms being based on user-generated content by nature (Amazon TV claiming a limited share at 6.8% of total company revenue). 

Why should brands that wish to grow not to limit themselves to walled gardens?

“Consumers are always looking for the next thing. So, our ability to scale audiences with any single partner and expect that we’re connecting with them throughout their entire journey, or all the way from the top to the bottom of the funnel and purchase, would be shortsighted if we only looked at one particular partner.”

Sean Peters
Chief Strategy Officer, Publicis Media

The old saying, ‘Don’t put all your eggs in one basket,’ is true. No social media has the capacity to fully scale up in every context and for all sectors, geographies and product cycles.

In the 1950s there was only one TV channel, which was fine back then. In today’s scattered offering, communication needs to be agile, relevant and smart in order to reach consumers with real impact, which includes not limiting your efforts to one platform.

Where are the media opportunities outside of walled gardens?

Broadening media selection in advertising activity can quickly become costly, as it requires producing creative content adapted to the other forms of media, objectives and universe chosen.

Digital media

The first, and most cost-efficient, step is to stick to digital advertising in the open web. That means websites and apps that exist outside of walled gardens. These represent over 50% of the time spent and benefit from a higher level of trust with the public.

IT offers a variety of buying mechanisms through programmatic solutions using third and first-party data for content outreach, contextual and publisher deals. The advantage here is that these use standard creative formats that don’t require further adaptation. The level of content and quality of environment differs greatly (as is the case with social media), but providers offer impression-level transparency and technical cost visibility to facilitate result tracking.

TV campaigns

The most powerful, and potentially expensive, is TV (linear, connected [link to Advance TV Article] and on-demand) as this format makes it possible to build advertising reach more rapidly. This used to mean straightforward buying of a few national channels offering powerful campaigns, leveraging expensive TV spot productions to generate leading campaigns that were then used for a few years. Today, TV is more scattered.

TV still represents the highest possible reach in the western world (94.1%of the UK population watches TV every week). Today’s consumer might watch sports and news on live television, dramas on subscription VOD and comedies through OTT. The implication of all this for marketers is that there are three key variables that need to be taken into consideration when building a plan: content, data and viewing platform. This means the creation of programmatic TV buy offers that combine these in a multiscreen plan.

Out-Of-Home (OOH)

Urban areas have seen the volume of digital opportunities to advertise in the streets growing. Considering new options to use media while on the go, OOH represents a good opportunity to target a younger audience that is more curious and willing to try out new things.

Cost of production is not high. Advertising campaign scale with time as the frequency is higher (once normal travel conditions have been restored) and new measurement technologies offer quality tracking solutions. As for TV, digital OOH (mostly urban areas) can be bought in programmatic platforms.

Radio and audio

Audio has been experiencing a transformation with the emergence of the podcast industry. (According to the infinite Dial, 37% of Americans listen to podcasts on a monthly basis.)

The development of digital radio and streaming have made music more accessible and digital audio extremely popular. As for other types of media, these can be bought on programmatic platforms.

Radio and audio are generally the cost-effective media of choice for traffic campaigns and low-cost reach, as they offer a great opportunity for brands to reach loyal listeners regularly. They also benefit from relatively cheap advertising production costs and quick turnaround. The efficiency of the media has long been proven by footfall measurements and regional testing.

Print (paper and digital titles) or publishing houses

Whilst Paper has been slowly on its way out over the past three decades, there are pockets of offerings that present valuable opportunities in a cookieless future:

A resurgence of specialized titles focusing on niche hobby and segmented content offers a no-wastage option to many consumer goods that attract offline customers

Contextual opportunities to address potential clients and build awareness in an expert environment while leveraging the trust established by the publishing provider

The quality of the associated content, which can attract a higher revenue crowd and generate a higher basket price for the same acquisition cost

We will be creating a series that will cover each type of media in 2021 and look at the benefits and constraints of each in more detail. Subscribe and stay tuned.

How can MMT help?

At MMT, our data analytics team supports your business needs when it comes to budget allocation and media efficiency based on market and client data. Whatever your business objectives, MMT defines the most relevant media mix that will move your business impact forward and scale up your business.

Our MMT Mercury media management software helps advertisers and agencies manage their digital campaigns end-to-end from planning to invoicing, including trafficking and monitoring across all platforms, walled gardens and the open web, connected TV, digital OOH, audio and digital print.

MMT Scout will help you visualize your performance and make faster decisions without spending hours gathering insight on multiple platforms. 

Other reads :

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I wrote this post for MMT

Media is a great microcosm for understanding the big data universe. Social media and the rest of the digital world provide an awesome amount of information and that information evolves considerably over time, in terms of granularity and utility for business performance.

Content Sections

• Why owning your data?
• What do we mean by code quality?
• Why is code quality so important for your solutions?
• Why hiring a media-tech expert can make a difference?
• How do we ensure code quality?
• How to use peer reviews as a company strategy for quality in software development?
• Summary

Why owning Data?

Marketers know that “data is the new oil,” but how do you harness the power of that information? By owning the data!

Owning the data entails specifically gathering, compiling, selecting, and understanding the data to which companies have access so that you can tap into effective learning that will lead to improved efficiency in media and business. You need dedicated instruments like software and data warehouses to get to that point. Although there are some off-the-shelf solutions and some companies would rather build a solution that meets their needs precisely, which makes sense for large corporations, this still requires hiring either an internal or external development team.

To the uninitiated, coding may seem opaque and read like hieroglyphs, and briefing a new agency to create a customized solution for your business can be daunting if you don’t have access to internal expertise. This should not be seen as a barrier to moving forward but rather as an opportunity to initiate a larger transition towards a better understanding of “coding.” Here are a few basics that will help you navigate what is at stake when it comes to creating a quality solution.

What do we mean by code quality?

Because code quality is a concept, it doesn’t have an exact definition. Code is used to program all kinds of modern solutions from sim cards to hospital administration solutions, which means it addresses a highly versatile set of requirements. This type of development therefore calls for standards.

We can use five points to summarize code quality:

1. The software/program does what it’s supposed to do and runs smoothly = reliability
2. The code is easy for an expert to read = maintainability
3. The code is well explained/commented on/documented = clarity
4. The code has a consistent style = consistency
5. The code can be extended = extensibility

Why is code quality so important for your solutions?

Now, why are those five pillars key to your code?

• Reliability and clarity – long-term maintenance – Optimized code is easy to update, upgrade and maintain in the long run. All software needs to be able to adapt to changes, even unexpectedly (thank you 2020 for reminding us of this).
• Maintainability and consistency – team changes and memory loss protection – Sometimes new developers join the team and have to work on other people’s codes. They need to be able to navigate and understand the logic to be able to work.
• Extensibility – upgrades and extensions – When the base code is “clean,” adding components to it is easy and it’s possible to develop extra modules and features without transforming the current applications.
• Time constraints – There are no limits to development capacity except time. If a company needs a tool next month, this obviously involves a higher risk of low-quality codes and will require more work to fix bugs and updates down the line. The tight deadline could also “stiffen” the adaptability of the architecture for future feature upgrades

“Some programming languages are more suitable for a specific task than others. Using the right language helps but often it is more efficient and better for the quality to select one that the team is more comfortable with over one the team needs to learn.”

Ole Reifschneider
Director Software Engineering

Why hiring a media-tech expert can make a difference Data?

There several factors that make the media and marketing industry special, the most relevant one for data management being COMPLEXITY. The industry lacks standardization when it comes to processes, data formats and dataflows. Each partner brings its own structure and naming conventions, which can hinder integrations. At the same time, media is an industry that never sleeps. It’s a global industry that runs campaigns 24/7 on multiple platforms, generating terabits of data daily, not all of which is useful. Data set needs to be compiled from various sources both online and off. These sources are extracted, compiled and organized so that marketers can visualize and compare the data. The relevant dataflows require provisioning, segregation, harmonization and calculation.

The nature of these flows also depends on the specified requirements, as different data will need to be extracted depending on whether the focus is on strategy, marketing, finance, invoicing, performance optimization. or auditing, etc… Bringing in a team with media expertise guarantees efficient orchestration of data deciphering. A data integration team that knows how to prioritize data by relevance will speed up your process immensely. That’s what our team does: the media agency world is our cradle.

How do we ensure code quality?

Each development team, depending on project size and complexity, uses one or more of the following techniques to ensure qualitative development.

• Automated test: A typical solution to confirm that the code is reliable (= runs smoothly) is to write a test to prove that all elements of the features are running as expected.
• Choose a coding language: Obviously, this requires the development team to be comfortable working in more than one coding language. Each coding language has its own benefits and even though choosing one adapted for the task helps, it does not make you more efficient if your team has to learn it first. Elm, for example, guarantees that there won’t be any crashes during runtime. But it’s still risky business to choose a language your team hasn’t mastered.
• Run static code analysis: This basic tool that all developers are familiar with helps ensure coding consistency and logic. Lint is one of the most famous programs. A side note: A lot of Lint tools even let you fix some code issues.
• Split app features into modules: Although this doesn’t really make things more flexible, it does make things easier to understand and limits side effects.
• Work collaboratively: Projects are shared between teammates (pair-programming), which brings more minds together to work on the structure and the logic to minimize initial static bugs. That includes the use of peer reviews. Leveraging the team’s capacity to read each other takes their work to the next level. We’ll go into more detail on this in the next section. 

How to use peer reviews as a company strategy for quality in software development?

Yes, peer reviews are standard. All software businesses with ambition and quality at heart use peer reviews before deploying updates, upgrades and features. I won’t take the time to list best practices as thousands of experts have already done. What matters is that peer reviews have become an integral part of our development team strategy, at the core of our way of working as a business. And we feel it makes sense to clarify our why.

It instills the ethics of sharing – The code is not yours or mine, it’s shared and should stay that way.

• Every member owns it and should be able to understand it. It’s not about lowering the bar. On the contrary, it’s about making things as clear and smart as possible so that you can take juniors to the next level while reviewing and learning in the process.
• It implies that the developers are keeping in mind that other people will follow up on their work and amend it as needed. Keeping a clean code is a sign of “caring.”
• It encourages each developer to listen and address all team member questions, which then helps you improve your solutions. It lets team members, old and new, share in the history and evolution of the project and products.
• We embrace the long-term vision of each product development with everyone responsible for the maintainability (= capacity to keep updating over time) and extendibility (= capacity to add new features) of the code. New ideas emerge when you know they will become a company legacy.

MMT benefits from a long-term perspective and growth plan. That lets us perfect the sense of quality that we want to see in the apps that we use daily. 

Summary

Data management calls for the right tools for it be useful, especially in advertising where data is plentiful but often too complex to understand and leverage. Building software to manage this data makes sense but requires selecting the right partners to address your needs qualitatively by bringing in sector expertise and a long-term strategy that will make your solution future-proof. Software needs to be more easily maintainable and extendable, survive updates and be flexible when it comes to upgrades. That’s why MMT focuses on the advertising industry where we have the proven expertise and experience that it takes to deliver the right solutions. 

Big thanks to Ole Reifschneider for taking the time to explain and share his passion with me. 

Also, learning every day – even for a minute – keep the spirit up and , so this is an essential reason to find moment daily to work on myself.

.. to complete Ghandi’s quote
Also, learning every day is key to my inner stability, so this is an essential reason to find moment daily to work on myself.

I remember reading a post – years ago, cannot find it again unfortunately but it was on this blog (in Spanish) – talking about waiting for retirement. The writer mention that story of a guy – 55 – who decided that it was now time to seat tight and wait for retirement. Sounds clever, hun ? That is without saying that Spain’s retirement age is 67! Would one really plan on staying idle for 12 years? The author was also deeply surprised, that the person did not even realise how long this would be.

Being conscious of one’s own idleness is a first step towards awesomeness.

Anyone can embark on the journey of change, but no change happen overnight: resilience and perceverance are paramount to any evolution.
Learning grounds me, humbles me by facing the deepness of my own ignorance – it helps me remain connected with the world surrounding you.
To me any goal can be compared a sport competition in the sense that training is unavoidable BUT without the physical aspect which limits the performance possibilities. Anyone can run a the distance of a marathon with training, but the time it is taking to do so depends on more factors than just training and will. That is where most people abandoned before starting. They believe that it is not worth running without performing.
I have been quite the opposite my whole life, jumping on every opportunity to learn something new to the point of embarking on a world tour or study wine making at 38, learning 6 languages (I can only speak four now) and living in 7 countries for more than 3 months.

Certain type of people just deeply believe that nothing is impossible. You can be part of this group. It is just about accepting to start somewhere on a journey of discoveries that will have positive effects, more often than you think.

Learning is not about reaching a goal, it is about making progress. Seeing one’s own progress is a great – Free – and personal reward.
If you need support in your journey, please do not hesitate to share here, support will come right back.