Data Strategy

Data Strategy Course

Section 2.5 Data Security Module
Hello and welcome to my course – on Data Strategy. This is Ade Awokoya from LBAcademy. I am a Digital Transformation Adviser, enabling support for Business to innovate on a digital platform, with a focus on your business model.

In this session, on Data Strategy, we’ll be covering the fundamentals and best practices of good data management, the technology that’s available to help and discuss how and when business should apply them as they grow.

Building your organisation data strategy

Creating a solid data strategy is one thing, but you need to implement it in practice to be successful. And this can only happen if you have the right team on board. Data science, machine learning, big data and AI are relatively new disciplines, and these new fields continue to be in short supply in the job market. This makes things difficult, since such, shortages create a situation in which human resources become very expensive, and it is hard to build a competent team of prepared professionals. This is a big issue, especially for your small /mid-sized business,due to limited resources

As they occur, results are often instantaneously visible and this can be incredibly rewarding. But even if it takes more time to see results, the impact that data can have on a business and the magnitude of such changes is very attractive.

Skills Matter for data strategy

There are several skills that every data focus team needs to have to turn data into insights.
They are business skilled analytical skills, computer science proficiency, solid statistics and mathematics,understanding and creativity.

1] Business skills: the data scientist should understand what creates value in the organization, which are the key performance indicators, how business variables are interrelated with each other. What makes sense from a strategic point of view and then based on all of this knowledge, extract meaningful insights from the data to suggest actionable adjustment that will create value in the long run.

2] Analytical skills: They have to be able to investigate cause and effect and reason with an open mind and spot patterns.

3] Technical proficiency: Computer science knowledge is a necessary prerequisite, since all activities; data collection, storage, analysis and communication are done with computers. This is a range of skills, including a variety of competencies, knowing how to collect data with sensors,how to store data in the cloud, how to run machine learning algorithms and much more statistical skills are also required for an organization’s data operations.

4] Creativity: Having an open mind and being creative goes a long way to increasing diversification of skills.

Building your organisation data competencies

Your firm will need to be creative to come up with the right solution. One possible strategy would be to recruit different professionals who have some of these skills, hoping that the individuals on your team will complement each other with their strengths.

Another viable option will be to hire data professionals who don’t have all five key skills and to train them internally with the hope that they would be able to learn fast enough. A general principle when dealing with human resources is that one of the most valuable traits a person can have is the ability and the desire to grow. The world of data is moving fast and new technologies and applications are emerging all the time, which means the ability to adapt and learn is becoming increasingly important.

Once you can identify individuals who have some of the five core skills described, then management will be able to tell whether it is reasonable to expect that these people will learn the skills that they lack. This can be a preferable option compared to recruiting someone from the outside. These individuals have a track record and people who have worked with them know their abilities.

Outsourcing your data needs

If you have studied business strategy know that in general, firms have to make a very strategic decision when it comes to specific activities related to their core value creation processes, produce in-house or outsource. When you build a data science team, you make a similar decision, hire people in-house or rely on external services depending on the situation and your particular goal. Either option can be right for you.

For example, if you struggle to recruit well-prepared professionals at a reasonable price, then it might be better to look for an external solution. Another situation when outsourcing is a viable alternative is when you don’t have any subject matter experts in-house. Deciding that you want to outsource is just the first piece of the puzzle. Finding the right partner and project manager is a bit more complex.There are also many smaller firms that have specialized in a particular industry, which gives them competitive advantage against bigger service providers.

You need to decide what type of service would work for you. When making such decisions, it’s a good idea to be as informed as possible to try to get in touch with some of the other companies that have worked with the data science consultancy you about to hire to see what their experience was like.

Ask for previous case studies and try to understand how the consulting company added value to their clients. This would be very helpful for you to make sure that the external partner you’re about to hire understands the industry and that you have defined the key deliverables and things like how much time you can expect with a contractor on a weekly basis.

Working with external experts is also a great way to improve the internal skills of your organization, make sure you focus on skills transfer when you partner with external consultants that will enable people in your business to learn from them. This, in turn, should put you in a position where you are able to rely more on in-house solutions in the future.

Data Strategy Leadership

As the key decision maker in an organization, you’ll need to take a leadership role to tackle the ‘data challenge’. This means addressing the issues that come when implementing a coherent data strategy in your firm, figuring out how data analysis will help your business, learning how to ask the right questions, building a team that can help the firm in its data analysis effort.

Selecting the right type of technology to be used, deciding whether to build in-house or outsource analytics capabilities. All of these topics are in large part the responsibility of a firm’s top management team. Leaders who ignore the paradigm shifts that are taking place will find themselves outpace and outdated in a very short space of time.

It is the role of business leaders to approach data strategy. And as a business leader, you have to define how data will help you create value, whether it will enable you to offer smarter products, smarter services, whether it will allow you to have smarter business processes, or whether or not to have a strategy that involves data monetization.

Don’t be afraid if you don’t know the nuts and bolts behind the different technologies used to execute your organization’s data strategy. You don’t need to be a tech genius to be a good leader. This is an ongoing process and I wish you the very best