What’s Big Data?
The word data refers to every quality (1, 2, 3), character (a, b, c), or symbol (@, &, ?) digitally stored on our smartphones, PCs, laptops, tablets, and similar devices. These are all the figures and facts such as videos, images, texts, numbers, and audios belonging to 5.22 billion digital users in the world. Based on the current numbers of tech-savvy users globally, the amounts of data are extremely big. So, the term big data is self-explanatory and marks the scope of data that is capacious, increases hourly, and cannot be analyzed or processed in a traditional way.
If the big data accumulates continuously and at a fast pace, what does it offer that is beneficial for others? For example,
- user-behavior data collection in the entertainment industry to propose better customer services
- real-time data collection to enhance security and prevent potential money theft or detect fraud in the banking industry
- valuable data collection in communication, healthcare, media, advertising, manufacturing, transportation, and retail industries
In order to make use of big data, businesses focus on raw storage, processing power, and strong analytical skills. The revenue from the global big data market is predicted to be $274.3 billion in 2022. The largest share of big data revenue will be generated from services spending, covering 39% of the market. The biggest whales on the global market that provide big data and analytics software are Oracle, Microsoft, SAP, and IBM. It is believed that by 2024 there will be 149 zettabytes of big data created worldwide.
Big Data: Classification
Big Data is classified into three categories:
1. Structured Data – 20% of information that exists in the database and is organized. Also, structured data is used in programming. It is divided into
- machine-generated: weblogs, sensors, financial systems like medical devices, GPS data, server-captured statistics of usage, etc.
- human-generated: personal data that is input manually (name, details, etc.). This data is used by companies to track customer behavior and support decision-making in businesses, and, later, relevant modifications.
2. Semi-structured Data – is somewhat organized and can be processed to a certain extent.
3. Unstructured Data – 80% of the data we see is not organized. It is also divided into
- machine-generated: satellite images, scientific data, radar data
- human-generated: social media data, mobile data, website content
So, basically, everything that surrounds us consists of data. Big Data and its analytics are used in building business strategies. It is a great tool to enhance your business and excel among your competitors. So, what does it mean to have a business strategy? Let’s find out.
Big Data Components
According to the University of California San Diego, there are five pillars every Dig Data Analytics works along with. These are important in building a business strategy and include:
- People. Employees or team members that are experts in solving challenges.
- Purpose. The main challenge your business has to solve.
- Process. The collaboration and communication process defines contributory steps such as: acquire – prepare – analyze – report- act.
- Platforms. Means (tools and applications) to carry out analytics and promote scalability.
- Programmability. The scalable process should be programmable, reproducible, supported with middleware, analytical tools, visualization environment, and reporting environment.
To sum up, if the 5 P’s are implemented and go well, we will achieve the 6th P, which is a Product.
Business Strategy Building
IMD Business School defines business strategy as “a clear set of plans, actions, and goals that outline how a business will compete in a particular market with its products or services”. Business Strategy aims to carry out the mission, vision, goals, and objectives of a particular business. It includes a detailed development plan that has predictions according to your growth methods and timelines. Predictions can be fulfilled or not making the plan sort of a guide towards success. If the creation of a good business strategy is more or less easy, implementing it is quite complex.
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Big Data Business Strategy
Successful Strategy is equal to business aim (setting goals), policy (setting standards), plan (enlisting the goals with the team), and action (being pro-active).
1.Business’ “Big” Objectives. To work with big data it is essential to find out what data is useful for your business and which data should be collected in particular. Define the goal of your organization and the data-related problems that have to be solved. Don’t be narrow with questions as this helps in analyzing your business gaps. The goals you set should be both supported with long-term and short-term objectives. In any case, you should evaluate how big data science and big data analytics will bring the company value with the help of data analytics. And you should figure out how the processes will be organized to move on.
2.Create an organizational mindset. Besides setting up the goals for the company, your vision of big data value should be supported by your team members. Thus, you are obliged to promote commitment, sponsorship, and communication within your organization. Commitment and sponsorship should go from the company leadership team. Thus, goals should be developed with all stakeholders and clearly communicated to every employee so that the value of big data was understood and appreciated by all.
3.Build diverse teams. Building a team is like building a foundation. A data science team should include data scientists, information technologists, application developers, and business owners, who are the necessary units to be effective and produce quality results. Of course, your team should possess a mentality set on common goals.
4.Build in-house expertise. One of the parts of the big data strategy is to constantly train your team members on business tools and analytics, business practices, and objectives. Especially, if your company is dependent on deep expertise in one or more subject areas. The experts working on the subject matter can be trained and provided support to achieve even more value. What concerns the other team members, it is important to make them understand what are the subject matters of the company and specify how he or they can contribute to this value using big data analytics and their soft or hard skills.
5.Generate and test ideas. It is essential to have a small team of experts within the company, who will generate business ideas and test them before deploying full-scale. This team aims at implementing new ideas to see what works for the business and what doesn’t. This makes the current team a strategic partner of an organization that brings value by analyzing data.
6.Share access to data. Data is an asset in every organization and within the organization. So, it is critical that team members within the organization have full access to it. The more open is the data-sharing mindset in your company the more it will bring value to future outcomes.
7.Define Big Data Policy. The big data strategy you’ve set usually defines all the policies around big data. The most important aspects to think about here are:
- Privacy and volatility. Who can have access to big data? How long does the data be valid?
- Quality and curation. Who is responsible for data curation? What makes the quality of big data?
- Regulation and interoperability. Are the legal standards met? Is your team cooperative and communicative based on big data?
8.Promote an analytics-driven culture. The mindset to establish should be grounded on analytics as an inseparable part of doing business. Analytics activities should revolve around business objectives and should be the focus of driving business decisions.
9.Use Case strategy adaptation. Big data technology grows rapidly and your strategy should correspond. To be able to adjust your big data strategy to the pace of technological progress and constant business growth, it is vital to be flexible and dynamic to meet any changes.
Thus, taking these steps of big data business strategy will enable you to produce a valid strategic output.
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Wrapping Up
Big data analytics strategy requires thorough planning and involves not only the business owners but all the team members of the company. Like any strategy, big data analytics needs every employee to adjust to the mutual mission and vision and aim to solve the same goals within the set timeline. Thus, carry out the steps enumerated precisely to set your business for success. If you’d like to receive some help with Big Data Services or strategic insights, we can give you a hand. Book a call with our CTO Brad Flaughter here.
