With data being present everywhere, it would be safe to say that the world today runs on data, making it one of the most valuable resources for business.
Statistics suggest that by 2025 we will be producing data at a rate of 463 exabytes per day. This kind of exponential data growth makes it crucial for businesses to effectively analyze the massive amount of data generated on a day-to-day basis and use it to make better business decisions that not just add value for various stakeholders involved but also enhance business ROI.
But this is exactly where the issue lies.
Simply moving data from traditional excel files to Power BI for data analytics or Tableau doesn’t cut the bill, as this is merely a shift in the platform and not the way data is being looked at.
While any company can take this step, bringing a shift in perspective that allows data to generate value for customers, employees, and overall business matters.
This blog aims to delve deeper into how to go beyond the traditional perspective of looking at data and embrace a more innovative and focused data-driven approach that enables powerful and fact-based decision-making to help you achieve better business outcomes.
There are numerous perspectives one can use to look at data. However, the best way to look at data is from the analytical perspective, as this is typically a scientific approach.
Besides, the analytical perspective of looking at data is incredibly important since it allows data scientists and data analytics companies to look at their data as specific features and observations instead of simple bits and bytes.
It is important to keep in mind that data is an intricate tool and is quite a big part of the data science field. This is why understanding data from more than one perspective is absolutely critical when it comes to ensuring business success and building successful projects.
It is quite likely that in some cases you might be able to get by and achieve what you wish for with a traditional or minimal understanding of limited portions of data. However, to get the maximum out of this valuable resource, expanding your horizons and shifting perspectives on how you look at data is important.
There has been enough buzz already about the power that data holds and about unlocking valuable insights from it to drive impactful business outcomes.
Data is often considered the most valuable resource that the entire world now runs on. Several top-notch companies, including Apple, Microsoft, and many more, have been built on the backbone of data.
However, a lot of data analytics companies still struggle to realize the full potential of data and extract value from it primarily due to being stuck in traditional ways of looking at data and the lack of a framework that can help understand and systematically increase data effectiveness to extract value out of it.
For artificial intelligence companies, it is important to understand that the power of data is directly correlated to the strategy and people of a business.
Data doesn’t belong to one single team or silo. On the contrary, data needs to be closely intertwined with different business processes, policies, and stakeholders. It requires reaching across business silos and seamlessly connecting together to bring out the right information, to the right people, at the right time.
Still, many organizations measure the power of data in a very basic way, which is mainly based on how big the data is. They typically measure their data along the following dimensions:
While this framework can help understand the scale/size of the data your business churns, it does not say much about the effectiveness or power of data to drive data-driven decisions and, therefore, the impact or outcomes of the business.
To leverage the real power of data, the need for artificial intelligence companies is to think out of the box and look for any unnoticed trends or anomalies in the existing data.
This can be possible only when there is enough time spent researching more and more to unleash the vital components of data that are often ignored due to the traditional understanding of your business.
When it comes to uncovering the true power of data, you need not focus just on the expanse or how big your data is but also on its effectiveness, that is, how useful the data is for bringing an impact on business.
One of the most effective frameworks here is the four Cs data efficacy framework that allows you to outline the various elements and changes in perspective required to turn huge bouts of data into high-value, decision-driving business assets.
These 4Cs include:
The first and foremost component is the consistency of data across multiple data sources or systems in which you store data.
For a particular data set, the format, definition, and value of data must remain consistent across sources/systems of data.
Another important requirement is the completeness of data. Data that is complete and verifiable is the essential beginning point for enhancing its value.
The completeness of data here includes many different dimensions, including precision, fullness, validity, verifiability, and timeliness of data.
Connected data means the linkage and persistence of relationships between various data sources/elements and how well they are defined and documented.
The completeness, consistency, and connectedness of data may still not be enough. It won’t ensure its effectiveness if it is not easily conspicuous to or discoverable by a wide variety of users/systems/apps to help them make business decisions or take system-embedded actions and create the desired impact.
A great deal of data that could also be useful for businesses is in inconspicuous forms, which is often hidden in hard-to-access patterns and needs much data churning.
An example of such data types is raw inputs to modeling data that data scientists use, which then undergo various data engineering processes requiring IT specialization to extract value from it.
Regardless of the sector, size, or type, the importance of data for an organization is immense. However, to get the maximum out of this asset, another vital component is to have a proper data strategy – a framework that allows you to draw/gain deeper business insights from the data to improve the bottom line of your business.
Whether you’re looking to transform your respective industry or wish to have continuous efficiency improvements, the first thing that you need to do is think strategically about your data.
Surviving in a competitive business landscape like today requires every company to understand how data works, how it can transform business models, and how to strategically provision for its acquisition, analysis, and leverage.
Failing to have a powerful data strategy will soon lead to new entrants with solid data-enabled propositions eating away at your revenues. An excellent example of this is companies such as Airbnb and Uber, which have disrupted their respective industries merely by being strategic in the use of data.
So, what is data strategy, you ask?
Data strategy is essentially a comprehensive plan to help you manage the data of your organization as a corporate asset. Simply put, it is an actionable and coherent set of steps, goals, concepts, analyses, policies, and reasoning specifically designed to unlock the value of data so as to help your business thrive in the ever-competitive digital world.
Apart from ensuring that your business gains a sustainable competitive advantage in the future with a positive rate of return on investment, having a data strategy is also important for the future wellbeing of the organization.
The lack of a strategic view of data will become an opportunity for your existing competitors to optimize and reduce costs as well as target the audience with more relevant communications.
Making data an afterthought can be fatal to the business as it reduces agility by delaying the launch of new insights, features, and products.
Some of the other key reasons why you need a data strategy are:
In the business world, it’s quite easy to get lost in a bad data cycle, especially when you’re trying to implement data-driven decisions in an attempt to accomplish new things using old/traditional methods and getting almost nowhere.
An example of this could be spending a great deal of time and money on technology without noticing any improvements as such.
Likewise, when you get trapped in a bad data cycle, you end up spending much more time discussing the data accuracy rather than the insights it provides and realize how difficult it is to give employees both the access and speed that they need or demand.
Some of the other signs you need to notice here include the inability to prove the value/worth of data or low trust in data from the business/leaders.
One of the best ways to break the cycle of bad data and reset your data journey is to build a robust data strategy that is in complete alignment with the business, along with a detailed action plan, clear value proposition, and a completely new perspective on handling data.
The exponential increase in organizational data in recent times has also led to a rapid rise in the number of new technology tools/solutions to help you revolutionize the way you handle/analyze your data.
However, the lack of a proper data strategy can quickly send you off track or distract you with traditional ways of going after unnecessary software or building useless dashboards for every data set.
In the worst-case scenario, you will be stuck in the cycle of going with quick-fix solutions instead of digging deeper to find the root causes or fundamental issues with data that you need to resolve.
A good data strategy can be instrumental in defining a clear set of goals and actionable steps to help you set the priorities right and get the most out of your data. It also helps machine learning companies avoid unnecessary distractions and focus on finding/solving the root causes of their problems.
Another advantage of a good data strategy is the distinct competitive advantage it gives to your business. The need here is to design an enterprise data strategy with concrete steps and actions to help you use data to your advantage.
Among these include effectively analyzing industry trends, recognizing what’s important, and taking decisive actions to take advantage of key business opportunities.
To ensure success with data, each action in your data strategy should build on the other to help you improve capacity and make decision-making faster, along with leveraging data-driven insights for key business ideas, higher ROI, and growth in opportunities.
Data science is essentially a method for gleaning insights from both structured and unstructured data using a range of different approaches, including statistical analysis, machine learning for business insights, and more.
In most cases, it is employed to transform data into value through reduced costs, higher revenues, business agility, development of new products, and improved customer experience.
This is precisely why an increasing number of organizations are using data science to improve their regular processes.
Some of the ways data science is helping businesses to use their data more effectively and run their operations in a better way are:
Traditional business intelligence that businesses have been using was more static and descriptive. Discovery through data science has transformed it into a more dynamic field and has rendered BI to incorporate various business operations.
As the amount and volume of data that businesses produce have increased exponentially, the need for data scientists to analyze and derive meaningful insights from such massive amounts of data has become more prominent.
These deeper and more meaningful insights allow data science and machine learning companies to analyze information at a much larger scale and help design better decision-making strategies, which essentially involve the following steps:
Data science platforms and data analytics consulting services can be instrumental in unearthing the hidden patterns from the plethora of data that your business generates and making meaningful insights, analyses, and predictions out of it.
This helps businesses (irrespective of their size) manage themselves more efficiently and benefit from data science to grow further.
Some of the other ways businesses can benefit from data science and data analytics consulting services include:
The importance of making better product designs to attract customers and build brand value is immense.
To achieve this, companies need to develop products that fulfill the unique requirements of customers to their desired satisfaction levels. Data plays an important role in helping organizations build their product in the best possible way.
Most organizations achieve this by analyzing customer reviews to find the best fit for the products, and such analysis is typically done using the advanced analytical tools of data science.
Apart from this, businesses across industries also utilize the existing market trends to make products most suitable for the masses. These market trends offer them insights into the needs of the consumers and come up with innovations.
Data science can be instrumental for collecting and analyzing data on a larger scale and can enable you to predict and identify emerging trends in your niche market. It allows you to do everything from tracking purchase data, influencers, and specific search engine queries to understanding customer preferences and knowing what kind of products people are interested in.
When you stay up-to-date on the behaviors of your target audience, you are in a better position to make profitable business decisions that allow you to stay ahead of the competition.
The best data science companies use data science to enhance the security of their data and protect personal/confidential information.
For instance, financial institutions use complex AI and machine-learning-based algorithms to identify anomalies and frauds in transactions based on variations in consumers’ regular financial activities. These algorithms are able to detect frauds faster and with much higher accuracy than manual methods, primarily because of the massive volumes of data generated every day.
Apart from this, AI and ML-based algorithms can also be used to safeguard sensitive or confidential information through encryption. Understanding more about data privacy can ensure that your organization doesn’t share or misuse customers’ sensitive information such as personal details, credit card details, contact information, and more.
All in all, data science plays a crucial role in businesses across a range of areas, including business intelligence, improvement of products, predictive analysis, and enhancing the management capabilities of companies.
In a nutshell, unleashing the full power of data requires breaking the traditional cycle of visualizing the same data in different ways and bringing a perspective shift to look at data.
You can do this by assessing the effectiveness of your current data and accordingly taking the necessary actions to fill the existing gaps and fulfill future needs in terms of data.
The need here is also to understand that this is a massive improvement that can only be initiated through detailed data assessments. These can fix any kind of immediate issues in terms of quality, integration, cataloging, mastering of records, schema redefinitions, governance, and more. This is crucial to create long-term opportunities for your business and relevant data usage optimization plans along with sustainable best practices.
Implementing such a continuous data efficacy improvement plan is the only way to build a powerful data-driven culture across the organization and get the most out of your data.
At 4Seer, one of the best data analytics consulting for small businesses, we understand the importance of data in unlocking better results for your business. We have several years of experience and professional expertise in helping you use your data to bring the concept of discovery through data science to fruition.
Contact us today to learn more about how the 4Seer platform can help unlock your data’s value and ensure greater data protection and privacy for your organization’s safety.
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