Why You Need To Integrate Your Data and Business Strategies

Liam Wilson our consultant managing the role
Posting date: 11/14/2019 9:25 AM

United we stand, together we fall. Not too put too fine a point to it, but how your business and data strategies align are integral to your business. Today’s world is about change, being able to pivot toward new strategies, and being open to trying new things. Consider this: the “mom-and-pop” shop is back and it is flourishing. Younger generations of farmers are returning to their family farms when they graduate and they’re bringing new knowledge with them. And the makerspace, freelance, and gig economies are thriving.

These businesses are learning how to work with technology and
align their Data Strategy with their Business strategy. Some legacy enterprises are taking notice. Others are missing the mark. Consumers may have changed how they want to shop and learn about services and products, but the services they want and expect haven’t changed that much which is why it’s more important than ever to “know your customer.” 

3 Key Elements of Integrated Strategies


While there are a number of things to take into consideration as you align your strategies, these three key elements can help get you started.


1. Understand the key elements of Business Strategy.
2. Apply innovation strategy to business objectives.
3. Determine key elements of your Data Strategy for use in a real-world scenario.

Understand the key elements of business strategy 


A business strategy encapsulates two main ideas; cost advantage versus competition. The cost advantage includes costs and other resources, identification and awareness of strengths, weaknesses, and competition. Competitive advantage happens when you’ve done your market research and can show what makes you different from any other provider with similar goods and services.

This is the time you might perform a SWOT (strengths, weaknesses, opportunity, and threat) analysis of your business. It’s helpful to include your mission and vision statements, objectives, core values, risk tolerance, and understanding trends in your business.

Apply Innovation Strategy to Business Objectives


Ideas and innovation flow when you and your business understand your customers and are able to easily shift into new things. Think R&D into Bioinformatics, automated tasks into AI, or a platform such as streaming services to help sell services such as insurance.

Laying the groundwork to apply innovation strategies to your business objectives follow these ideas:

  • Identify your business objectives by asking questions.
  • Assess the budget and personnel resources and develop a budget strategy.
  • Test the market to determine what issues will or need to be solved and understand how this innovation will benefit your overall strategy.

If you’re working on a Data initiative to integrate into your Business strategy, one of the key elements is to determine how those changes may affect your business.

Determine Key Elements of Data Strategy for Use in Real-World Scenarios


As you work on developing your Data Strategy, it’s important to consider all the elements required to ensure success. So, what do you need to take into consideration when working on this type of strategy? Here are some things to consider as you develop your framework.

  • Determine your business needs and their current state.
  • Determine what works and what can be improved upon if there is a technology improvement or process.
  • Evaluate your Data from sales, profit, and evaluate your progress.}
  • Develop an action plan.

Many businesses don’t incorporate just one type of Data into their strategy. They consider the potential impact of technologies such as Machine Learning, Predictive and Data Analytics, and other Big Data Strategies to drive improvements when it comes to decision making. They understand these Data-driven insights can help them improve or solve their most critical problems.

There is a caveat, however, and it is how you collect the information for real-world scenarios. Certain requirements are in place for a reason and they ensure only relevant Data is collected. This is done by formulating “predictive models” and necessary information to operate and determine whether your case will be something to be done over time or if it’s something brand new to consider when looking at real-time access.

One Final Thought…

Data-centric organisations have a distinct advantage over their competition. The information gained from collecting and analysing to understanding their customers can offer great insight as to what’s working and what isn’t. Integrating your Business Strategy with a Data Strategy can offer you a more well-rounded understanding of the customers you serve and can ultimately, help you to serve them better; now and in the future. Disruptive business models from this way of thinking can also foster growth and lead to innovative changes in your marketplace. 

If you want to be at the forefront of change we may have a role or candidate for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

It Takes Two: Data Architect Meets Data Engineer

Information. Data. The lifeblood of business. Information and data are used interchangeably, gathered, collected, and analysed to create actionable insights for informed business decisions. So, what does that mean exactly? And to that end, how do we know what information or data we need to make those decisions? Enter the Data Architect. The Role of a Data Architect Just like you might hire an architect to sketch out your dreamhouse, the Data Architect is a Data Visionary. They see the full picture and can craft the design and framework creating the blueprint for the Data Engineer, who will ultimately build the digital framework. Data Architects are the puzzle solvers who can take a jumble of puzzle pieces, in this case massive amounts of data, and put everything in order. It’s their job to figure out what’s important and what isn’t based on an organisation's business objectives. Skills for a Data Architect might include: Computer Science degree, or some variation thereof.Plenty of experience working with systems and application development.Extensive knowledge and able to deep dive into Information ManagementIf you’re just starting your Data Architect path, be prepared for years of building your experience in data design, data storage, and Data Management. The Role of a Data Engineer The Data Engineer builds the vision and brings it to life. But they don’t work in a vacuum and are integral to the Data Team working nearly in tandem with the Data Architect. These engineers are building the infrastructure – the pipelines and data lakes. Once exclusive to the software-engineering field, the data engineer’s role has evolved exponentially as data-focused software became an industry standard. Important skills for a Data Engineer might include. Strong developer skills.Understand a host of technologies such as Python, R, Hadoop, and moreCraft projects to show what you can do, not just talk about what you can do – your education isn’t much of a factor when it comes to data engineering. On the job training does it best.Social and communication skills are critical as you map initial designs, and a love of learning keeps everything humming along, even as technology libraries shift, and you have to learn something new. The Major Differences between the Data Architect and Data Engineer RolesAs intertwined as these two roles might seem, there are some crucial differences. Data Architect Crafts concept and visualises frameworkLeads the Data Science teams Data Engineer Builds and maintains the frameworkProvides supporting framework With a focus on Database Management technologies, it can seem as though Data Architect and Data Engineer are interchangeable. And at one time, Data Architects did also take on the Data Engineering role. But the knowledge each has is used differently.  Whether you’re looking to enter the field of Data Engineering, want to move up or over with your years of experience to Data Architect, or are just starting out. Harnham may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  

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