Data Science Strategy – How to properly develop a strategy based on collected data
- The value of data science
- What is a data science strategy?
- How to define goals and sources of information
- Data science strategy pieces
- Three steps for a successful data science strategy
- How does the wrong strategy affect your business?
- Data science at SoftFormance
- Should you hire a data science team and where to find experts?
- Wrapping Up
A full-scale revolution is happening right now caused by the use of data science in most industries.
Machine learning, AI, and automation tools are used in targeted advertising, eCommerce, internet search, education, medicine, video streaming, and dozens of other fields.
Organizations that aren’t investing in data science will likely soon be left in the shadow of their competitors. In fact, the Big Data Analytics market is projected to reach $105.08 billion by 2027, and small and medium-sized businesses are seen to drive the market’s growth.
Indeed, utilizing new technologies like machine learning and AI will boost your business growth, simplify operations, and even drive down costs, but you can’t just start using them without having a strategy.
You need to know how to invest in data science wisely, where to look for data, how to extract the most valuable insights, and how to utilize the data for it to be beneficial for your business.
That’s why you need a data science strategy. It will help you find the most effective data science team, build a great data architecture, and approach the commercial aspects of data science the right way.
In this article, you have learned the theory of data science, and now it’s high time to learn about its practical part – building an effective strategy. If you’re ready to start taking the most out of machine learning and AI, keep on reading.
The value of data science
The amounts of available data are overwhelming, but does this mean that all of it is valuable? Data itself is not valuable, but once you define the problem you need to solve and find the right data to extract insights from, it becomes priceless.
What is data science? It’s the process of collecting useful information out of all the data there using different tools and techniques.
Data science has a five-stage life cycle:
You can read in detail about data science, its uses, and benefits here but let’s summarize it shortly again. Thus, data science:
- Increases business predictability
- Boosts innovation
- Improves the decision-making process
- Favors the marketing and sales area
- Ensures real-time intelligence
- Improves data security
As you can see, data science can create lots of value for your business if implemented right. That’s why data is often called “the new oil” of the 21st century. It drives much of the transformative technology these days with the help of machine learning, AI, advanced analytics, and automation.
Data can also help replace old solutions with new ones, create new services, and continuously improve your business operations.
Having a data science expert in your team will help you solve complex issues that cannot be measured directly. They break a process into factors, which helps determine to what extent each factor contributes to the problem.
What is a data science strategy?
A data science strategy is a company’s vision of how and what elements of data should be used to achieve one’s business goals. Moreover, it’s about the ways to build a thriving data culture within an organization and where and how to get the skills and knowledge required to execute this vision.
For example, let’s say that you have decided to build a house. To do it, you need to find a professional team of designers and constructors, buy all the necessary tools and building materials, and, most importantly, have a detailed plan of the house.
You won’t be able to build a house if you just have bricks and nails just like you won’t be able to do it without the builders. Great results are only possible if all elements of the strategy are in the right place because they depend on one another.
The same applies to your data science strategy. Its success depends on the codependence of all of its elements and your ability to use them wisely.
How to define goals and sources of information
Any business strategy should start by asking yourself a question: what are my goals and what do I want to achieve in the first place?
All businesses have the same end goal – to generate more revenue, but their paths there are different and depend on the business owners’ decisions.
To use data science to its fullest potential and see its real benefits, you need to set a measurable goal.
For example, let’s say that you work in eCommerce, and your company is facing some customer conversion issues. Your main measurable goal is to increase conversion rates and boost current statistics. First of all, you need to check the current conversion rates, bounce rate, user flow, and conversion funnel in the analytics software. This will help you identify where the problem lies.
Once you identify the main issue, you can see how to achieve your goal of improving conversion rates. You can either ensure the most popular items on your website are easy to find, the search process is smooth and easy, and that there are enough filters so that users only get those choices that meet their needs.
The key is to identify clear goals, and then it will be much easier to build a strategy and achieve those goals. Once you have a measurable goal, you will understand what can be done with all the data you have and the insights you extracted from it.
Data science strategy pieces
Once you define a measurable goal and identify potential data sources, it’s time to start building your data science strategy. It can consist of various pieces, but let’s focus on the 9 main elements.
The first element of a data science strategy is vision. Building an effective vision is essential if you want your team to stay motivated, open to change, and move in the same direction. Without a clear vision and a data science mission statement, you risk being overly focused on short-term goals and facing multiple everyday challenges.
A great vision is one that aligns with your company’s mission, focus on successful outcomes, and is not too complicated and overly technical. If you want your vision to be clearer and more well-defined, you can support it with mission statements and value statements.
Always remember that vision should be the first element of your data science strategy. All the next elements can interchange, yet vision should always remain the first. It should serve as a guide that helps you define the rest of the plan and where to move next.
For example, IKEA’s vision statement sounds like this: “Our vision is to create a better everyday life for many people.” It makes it clear that they are in the market to offer high-quality goods that suit everyone’s lifestyles for low prices.
In SoftFormance, when we first meet with the client, we talk through their vision and mission statements or help them develop them. Before moving forward with the next elements, we ensure that our vision and the client’s vision coincide and that there’s no misunderstanding on any level.
According to the NewVantage Partners Big Data and AI 2021 Survey, “the greatest challenge for leading companies in their efforts to become data-driven continues to be due to cultural barriers – 92.2%”
That is why if you want to build an effective data science strategy, you should identify organizational and industry cultural challenges. These challenges include:
- Business processes
- Organizational alignment
- Change management
- People skillsets
- And a lack of understanding
An effective data science strategy can also help you see how data can support your employees’ individual motivators and how to use it to support your company’s values.
For example, you can organize a data science club, recruitment events, dev discussions, and other events to support communications within the enterprise.
At SoftFormance, we frequently organize events like that to allow our employees to communicate with each other, share their experiences, and build closer relationships.
When you’re building a data science strategy, it is also important to think about who will work on your project and each person’s role.
Usually, company leaders choose to stick to the agile team structure which we have covered in this article.
A typical team structure consists of a project manager (PM), a business analyst (BA), UI/UX designers, developers, and a quality assurance engineer (QA). However, you are free to add more team roles depending on your data science strategy.
When creating a team, always consider these questions:
- Who will be responsible for the vision and its execution? If you have a larger organization, your choice may be the Chief Data Officer. However, if you have a smaller business, hiring a Data Science Team Manager could be a great idea.
- How will you structure data: in a centralized or a decentralized way? Which one would be more effective for your project? Regardless of the choice, make sure that the communication strategy is aligned among all team members.
In 2017, the Economist declared data as the world’s most valuable resource.
Moreover, almost all of the top companies worth over 1$ trillion are built on strong data strategies as you can see in the table below.
|Company||Market Cap||Strong Data Strategy|
|Saudi Aramco||$2.105 T||Oil|
|Alphabet (Google)||$1.290 T||Yes|
These are the world’s top five most valuable companies according to this website.
As you can see, all of them except Saudi Aramco take data as their most valuable asset, and their market cap only continues to increase because of that. This means that having a strong data science strategy allows one to capture, store, and retrieve data more effectively.
If you want your data science strategy to be effective, make sure you make data FAIR.
- Findable – data and metadata should be easy to find by people and machines.
- Accessible – everyone can access the needed and appropriate data, be it authorized or unauthorized users.
- Interoperable – the sets of data should combine easily with each other.
- Reusable – data sets should be reused constantly.
Technology is constantly evolving, especially in the field of data science. Thus, it would be a huge mistake not to follow the trends and be left behind. Your goal should be to constantly discover and purchase the evolving data science tech stack.
For example, the top technologies that can be used in data science include:
- Amazon Web Services (AWS). AWS is a cloud computing service. These services include Amazon Machine Learning, Amazon Redshift, Amazon Simple Storage Service, and Amazon Rekognition, Amazon Textract.
- RFID (radio-frequency identification) and NFC (near-field communication) will allow you to use the Internet of Things (IoT). It’s a new network of physical objects embedded with software, electronics, and sensors that help them collect and exchange data via the internet. You can use them to predict maintenance or determine a customer’s risk profiles for various incidents.
- Natural Language Processing. With this technology, you can use Text Mining for your data science projects. This practice is about extracting data from text-based information such as documents and articles. It is widely used in industries like law enforcement and healthcare. NLP is used for extracting valuable information from unstructured information, discovering hidden topics, and sentiment analysis.
- Stream engines like Apache Kafka, Spark stream, and Flume, can be used for Streaming Analytics. It allows data scientists to analyze data in real-time. During this process, experts can have a deeper insight into the events as they occur, which helps organizations stay proactive.
New technology allows companies to collect and retrieve data faster, in bigger amounts, and, as a result, be more effective and competitive. Thus, ensure that your tech stack provides your team with effective machine learning tools and resources.
Product management is essential in data science. When working on a strategy, you need to ensure that you focus only on those opportunities that will work out and bring you the most benefit, deferring the other ones not to waste time and resources.
To achieve this faster and easier, invest in data science product management. Your data science strategy needs to outline the type of research and the type of products your data science team will deliver.
That is what a data science product manager does. This person works with the data science team, data engineering team, and product development team. The tasks of a data science product manager include:
- Understanding customer needs.
- Figuring out ML solutions.
- Identifying good use cases.
- Understanding business needs.
- Launching new products on time.
You can use the Data Science Product Manifesto to help you create a strategy easier.
A data science lifecycle is different from the software product lifecycle. Thus, that is why you should identify a strategy behind it and decide how you will manage your data science projects.
Usually, a data science lifecycle consists of these steps:
- Capture. This stage involves data acquisition, data entry, signal reception, and data extraction.
- Maintain. Next comes data warehousing, data cleansing, data staging, data processing, and data architecture.
- Process. Data mining, clustering/classification, data modeling, data summarization.
- Analyze. Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis.
- Communicate. Data reporting, data visualization, business intelligence, and decision-making.
Always make sure that there is a collaborative framework for your team to use and know how to coordinate their tasks. Moreover, it is essential to adopt agile principles and make all the processes repeatable.
Machine learning operations
Machine learning operations, or MLOps are rapidly gaining momentum among Strategic Data Scientists. These are the reasons why this is happening:
- MLOps unify the release cycle for machine learning release.
- MLOps allow applying agile principles to machine learning projects.
- MLOps reduces technical debt across machine learning models.
- MLOPs allow to automatically test machine learning artifacts.
When you build your machine learning operations, consider operational strategy and processes, model management, cloud systems management, and data management.
It is impossible to execute a strategy without a roadmap. You need to set priorities and work on some aspects of the plan first and then use them as building blocks. This will allow you to assess how much time it will take to execute a project and how to communicate priorities.
What are the prerequisites for a good project roadmap?
- A good validated idea
- Product strategy
- A trusted development team
- Team roles: programmer, business analyst, UX/UI specialist, and project manager
Three steps for a successful data science strategy
Now that we have covered the basic elements of any data science strategy, it’s time to focus on the steps you need to take to ensure that this strategy will be ab effective one. You need to find ways in which AI will help you be two steps ahead of your competitors and will benefit your data science strategy the most.
These tactics include such steps: aligning with a vendor that will support you and your team, gathering the right information and determining which metrics to track, and, finally, setting goals that will show valuable results.
Align with the right vendor
Building a successful data science strategy is not easy, and you don’t want to work on it alone. It’s always a great idea to find a vendor to partner with, someone who will support you and your team and help you build a successful strategy for your business.
We have already talked about how to find the right tech partner for your business here. Shortly, you need to find a vendor who understands your business specifics, aligns with your business objectives, and, most importantly, has a similar experience to your project requests.
It also needs to be someone who values communication and is committed to your project.
Finally, as you develop your data science strategy, you need to choose a vendor who values continuous growth and has enough expertise in the strategic data science field.
Gather and track the right metrics
Having all the right tools is not enough to create a successful data science strategy. You also need to track the key metrics if you prioritize the return of investment and long-term value.
Here is a list of metrics to keep track of:
It is a great idea to track component reuse as a KPI. Reusing components created by your data science team allows future models to be produced faster.
Moreover, reusing valuable data, using standardized templates, and preserving effective software configurations allows your team to work smarter, produce results faster, and make fewer mistakes.
Another metric that is preferable to track is your team’s productivity. What I mean here is setting targets to measure productivity, and data scientists do it to prove that they are not wasting time every day.
For example, your team can report on the data they collected from a user activity database or create a base model with 70% accuracy.
Set use-case-driven goals and demonstrate valuable results
Use-case-driven goals closely align with the tactical part of your data science strategy. These goals can look like this:
- Increase sales as compared to the last couple of months
- Train your data science team on the new type of machine learning algorithm
These goals are not strategic, they are operational because they are the small milestones that your organization needs to meet to achieve long-term success and strategic vision.
Keep in mind that with data science, you won’t see immediate results. That is why you need to set those daily goals to track progress and see the results sooner.
Create a template
A good idea would be to create a data strategy template. It will help you focus on your priorities and have the essential elements of your future strategy in front of you.
Take a look at this data science strategy template. As you can see, it consists of 5 use cases, cross-cutting goals, data requirements, technology, skills, etc. Let’s talk about each element briefly.
First thing you need to do is fill out the Data Use Case Template for each of your future projects. It will help you prioritize your data science projects and know exactly what you want to include in each of them.
Here are a few examples of data use cases/projects:
- Optimizing prices
- Automating the production process to make it more efficient
- Identifying smarter services or products
- Delivering a better, more personalized user experience
Now, look at this part of the template. In this section, you need to identify what data you are going to need and how you will source it.
For example, you may focus on data diversity for all of your data projects. This means that you will combine different data structured and unstructured, for instance) to ensure that you get a full picture and more possibilities.
This section is about data quality, ethics, ownership, privacy, and security. These issues may arise in all user cases, so they are cross-cutting.
For example, data quality or privacy issues can be a problem across the whole organization. Thus, to achieve your data priorities, you need to solve these issues. You need to ensure that data is accurate and that no one’s privacy is violated.
In this section, identify the cross-cutting issues that relate to technology, or the software requirements that are common across all use cases.
Identify what technology you would need to collect data, store it, process it, and communicate insights.
Sometimes, you might already have the software required for all of the processes, but it can also happen that you would need to invest in new software.
Skills and capacity
Usually, the lack of skills and knowledge of data science is a common issue in many organizations. That is why you would need to have cross-cutting requirements for closing this gap.
To ensure that these issues are addressed, train your staff or outsource your data science processes to external experts. You can also work with a data provider.
You should also think about the common issues that might prevent you from meeting your goals and turning your plans into reality.
For example, your teams may not be as excited about the data science implementation in your business. Thus, a requirement would be to invest time and money into educating managers and teams on the benefits of using data.
This data science strategy template and leadership in your niche can make a great deal in the world and help you be ten steps ahead of your competitors in the market, so use this template to your benefit.
How does the wrong strategy affect your business?
Just like a great data science strategy can lead you to success and be two steps ahead of your competitors, so can a wrong strategy have severe effects on your business. Thus, it’s better to know about the possible adverse effects as they will help you focus more on the tactics and each element of the strategy.
Lack of objectives
If you don’t have a coherent strategy, it is impossible to identify business objectives. Without these objectives, you wouldn’t know where to move next and what processes to focus on. Thus, it’s highly unlikely you would be able to move forward and grow.
Moreover, if you have no clear objectives, how do you know when you accomplished them? That’s why it is so important to focus on your business objectives and find a tech partner who will understand them.
For example, that’s how we started work with one of our clients – Opesta.com. On the first call, Ethan and SoftFormance’s team realized that our business backgrounds coincide, and we see a clear roadmap of what to do with Ethan’s idea next.
Resources not properly allocated
One of the reasons why businesses create strategies is to allocate corporate resources to their projects and operations that require those resources the most.
If there’s no proper planning or it’s not coherent, it is almost impossible to create budgets for business projects and understand how much money you will need to hire a team and develop your app.
Dispersing funds randomly is not the best idea as you can easily find yourself short of funds and have no more money for essentials such as paying your vendors.
Unclear organizational structure and communication issues
As we have already mentioned, one element of a successful data science strategy is clearly identifying the team roles. The number of people on the team and their responsibilities depends on your unique strategy and approach, but it is essential to make sure that you have a clear organizational structure.
It will ensure that each member of the team does their job, everyone collaborates and communicates with each other, and that there’s consistency and coherence to the team’s work.
In SoftFormace, we meet every Monday and discuss the agenda for the following week. On Wednesday, all team members send mid-week reports in Slack channels about how much work has been accomplished and whether everything goes well.
On Friday evening, team members send weekly Slack client channels, including info about all weekly progress, issues, and achievements.
Thus, each member of our team understands their role, responsibilities, and tasks, and frequently sends reports about their progress.
Data science at SoftFormance
At SoftFormance, we believe that because of the many data science benefits, it promotes growth and boosts effectiveness, and our aim is to apply it to as many of our projects as possible. In recent years, data science has helped us successfully finish projects like DashPro & AdsPro, Revenue Compass, Opesta, LocalPower, and others. Let’s talk about them shortly.
DashPro is an analytics platform for Solar Panel Agency. There is a Dashboard tool to collect and adjust data from different sources, like Facebook and SalesForce. That is where data science was applied – we created a tool for the collection and analysis of data as well as for extracting valuable insights for Solar Panel Agency.
AdsPro is a Facebook ads management tool for one of our previous clients. It allows setting up FB Ads Customer Segments API and Bid Multiplier APIs, with a custom ads builder. We used data science to identify customer segments, analyze reviews, and recommend specific products.
Revenue Compass is the fastest, most advanced, and most user-friendly commercial intelligence platform designed to enable fast-moving consumer goods manufacturers to reduce enterprise-wide price leakages, optimize trade spending and maximize value capture by charging every channel partner prices that are uniquely right for them.
Opesta is a marketing automation SaaS app that instantly increases open rates, click-through rates, and conversions by providing everything you need to generate leads, market, and sell using Facebook Messenger. We utilized data science to identify a potential customer base, for forecasting their potential needs, and analyze what would sell best.
LocalPower is a roof design tool with the automated best placement of panels on top of it. We have created a CRM for solar panel agencies with a unique Roof Design Tool and proposal. Moreover, we have worked on front-end solar software, modeling, proposal, and project management applying data science.
Should you hire a data science team and where to find experts?
If you decide to work on a data science strategy, you need a team to help you. It can either be an in-house team or you can decide to work with outsourced experts.
Taking into account the number of data science experts in the global talent pool, outsourcing seems a smart option. This way, you can choose the best of the best, save money you spend on the hiring process, increase efficiency, and have more access to innovation.
Furthermore, hiring an entire data science team from scratch can take months, while working with an outsourced team is easier and faster.
Where do you find data science experts? I have already answered this question in detail here. Shortly, if you are looking for a generalist data scientist, don’t throw out someone’s resume just because this person has a different degree or is from a different field than what you expect.
Remember that data scientists come from everywhere! They are very diverse in their education and background. There are numerous data scientists who have backgrounds in software engineering or data analysis roles, which are very common pathways to data science.
Ordinal data vs. nominal data: What’s the difference?
Original data is a type of categorical data that has an order. Thus, all variables in the original data are listed in an ordered manner and they are usually numbered. For example, it can include positions in class like “first” or “second.”
Nominal data, on the contrary, is used for naming or labeling variables, without quantitative value. Thus, there’s no ordering to its variables. Examples of nominal data include race, gender, country, hair color, etc.
Consider this example:
- How was your experience with our customer service?
- How was your experience with our customer service?
The first is the example of nominal data, and the second – ordinal data.
How long does it take to gather information for a strategy?
The amount of time it takes to collect data depends on many factors: your project’s scope, data availability, project tasks, and unexpected events. Thus, to answer the question “How long will this take?”, first determine the scope of the project, then ensure that there is enough data available, define the high-level tasks and see the estimated time for each task, and apply a fudge factor (time spent on unexpected events, on meetings, answering questions, etc).
Who are the Data Science experts?
A data science expert is an analytical expert who uses both technology and social science to manage data. A data scientist structures and manages data using computer science, statistics, and maths.
Adding data science, machine learning, and AI to the technologies you are already using is a brilliant idea because it will allow you to keep up with the times and stay competitive in the market and save a lot of resources.
Every business needs a data science strategy right now, big or small. It allows business owners to have a clearer vision of their company’s future, the possibilities for growth, and ways to attract new clients.
It can seem that building a data science strategy is difficult and requires a lot of time and resources, but it’s not entirely true.
If you define your goals, choose a team of experts who will help you, and focus on your tasks, you will be able to build an effective strategy with little to no challenges.
This way, you will avoid unnecessary problems, will know what to expect from the final product, and all members of your team will know their roles and responsibilities.
A great strategy saves you time, and money, and protects you from unnecessary stress, so why risk it?
We have already helped dozens of our clients with building a data science strategy for their business, and we are ready to help you, so if you want to work with us or need a consultation, just contact us.