Why Enterprise Brands Need a Good Data Strategy
As data continues to grow, there is a tendency for the data analytics process to become complicated. Exponential growth in data would mean an increase in both structured and unstructured data. This could lead to complications when the business needs to get insights from the data collect. Data analysts try to achieve simplicity and flexibility when working with data. They ensure a simple environment that requires less administrative work and maintenance. This affords them immediate access to the data they need.
Drowning in Data Strategy
Data is obtain from different channels including mobile and IoT devices. Truth be tell, it could get overwhelming. With so much data being produced by the minute, it is important for brands to discern which data is important valuable and which isn’t. Enterprises are drowning in data simply because they don’t know how to swim. Oftentimes, organizations approach their analytics blindly. They look through loads of data hoping to find gems.
One good strategy for cleaning good data is that the brand should sift through the data created in various channels. It is a good idea to separate good data from useless data. Having an effective data strategy enables brands to analyze the data and get actionable insights.
Aligning Data Strategy with Business Strategy
Effective data strategy is one that works not only for today but also for tomorrow. For that to be the case, the strategy would prepare for required future changes within the organization. Functional structures, processes, and policies need to be put in place. Such a journey would need executive management backing.
Increased Adoption of AI and ML
Artificial Intelligence and machine learning are emerging areas that enable brands to collect and make use of unstructured data. In the past, unstructured data was difficult to collect. This is achieved through Natural Language Processing (NLP) or Optical Character Recognition (OCR).
The increase of AI-enhanced applications helps brands to better utilize the data they collect. It will also help the marketing department to demonstrate the value of their efforts. If properly trained, AI-based marketing will enable marketers to get actionable data from email, social media, and other sources of customer data. It helps businesses to better personalize their services and improve their communication and targeting.
Can Data Expire?
It is important to know that data has an expiration date. For example, data collected before the Covid-19 pandemic is no longer useful for making business decisions today. Most of the market conditions that prevailed before the pandemic do not hold anymore. Therefore, data collected at that time are now obsolete. There has been a great shift. To make the most use of data, data collection processes need to factor in time.
Brands need to structure a situation in which their data strategy is business-driven. When they do this, the data collected can be useful to the business when the need arises. Data being collected is for the purpose of the business- to enable data-driven decision-making.
More and more organizations tend towards the principles of data democratization to enable their people to utilize information. It helps them to get insights and form a long-term strategy. This means that a solid data strategy should be in place to support the effective use of information. They also embrace data literacy, which is at the heart of data democratization.
Questions Brands Should Seek to Answer
Two brands need to evaluate data from a quantitative and qualitative standpoint. Some of the questions they should seek to answer are focused on the way customers interact with the brand. They could also seek insight into brand recognition. Analytics helps brands to answer questions such as: How much the size of the market compared to the competition. Data helps brands to identify the best positioning that would make customers choose them over the competition.
With so much data coming into brands pipelines from different channels daily, many are beginning to drown in data. They are drowning because they cannot swim. By creating a data strategy to obtain clean data and taking advantage of technology to analyze the data, they can begin to answer the big questions.