5 Characteristics of Data-driven Businesses
For decades, businesses based their most important decisions on gut instincts. While few things can replace the know-how of a skilled C-suite executive, today’s companies can make more informed decisions using hard data.
Transforming a company into a data-driven business requires more than just advanced data collection tools. The entire company culture must embrace data throughout its work processes. Below are a few key tenets of a successful data-driven business.
Centralized Data Sources
Every business process produces endless amounts of data. When this data is spread out across departments, it is impossible to pull out relevant trends or develop a coherent picture of the company’s performance.
Data centralization is the process of collating data onto a single, accessible platform. In a centralized system, all employees are involved in data preparation, management, and governance. This prevents many of the pitfalls of big data, such as duplications, data errors, or outliers.
Merging data from several departments and workflows together can quickly become unwieldy. Sophisticated data dashboards solve this problem by transforming raw data into visual representations. For example, data derived from Amazon Intelligence can provide insight into sales, logistics, order status, and operation costs.
Without a data savvy workforce, companies cannot make informed data-driven decisions. In the traditional business model, IT training was limited to specific departments and roles. Today, employees at all levels need to engage in ongoing data science training.
The basic principles of data management do not require an advanced understanding of math or computer science. Instead, training focuses on learning data tools and developing an analytical mindset.
In workforces with large disparities in data literacy, mentoring programs can support employees facing a sharper learning curve.
Robust analytics processes ensure that data collection leads to useful insights. Analytics may be used to assess past, present, and future decisions. Data analytics can also identify the driving factors of positive or negative outcomes or predict future performance.
Effective data analytics begins with a specific question. For example, a business may want to understand past campaigns or analyze market growth over the past five years. After translating the goal into a question, businesses can select the most relevant data sources.
Employees, with the support of data analytic experts, can collect, clean, and review the data for analysis. Following the analytic process, the data and the business solution is translated into easy-to-understand visual mediums, such as graphs, charts, or maps.
Comprehensive Data Policies
Data-driven companies create their policies based on data integrity and quality. Data governance plays an important role in this process.
Data governance has four main pillars:
- Stewardship: While data should be shared amongst all team members, certain individuals must take accountability for the data. These roles are often filled by data specialists and managers. Data stewards ensure that data is accurate, secure, and updated.
- Management: Raw data is rarely organized. Management systems sort data into searchable databases and formats. In addition to housing the direct data, these systems can also generate metadata, which can give company’s insight into the data collection process itself.
- Use Case: Quality data is an asset for businesses. It can be applied to other processes beyond business decisions, including generating mandatory reports, meeting regulation requirements, and implementing system wide upgrades.
- Quality: Poor quality data can lead to inaccurate conclusions. Data policies should include data maintenance and cleaning processes to remove duplicate or outdated entries.
Data policies should also respect user privacy. Businesses should inform customers, vendors, and any other data sources of how their data is collected, stored, and used.
The world is on track to generate more than 460 exabytes of data per day. Automated data collection and processing allow businesses to keep up with their immeasurable pools of data.
Data-driven businesses invest heavily in automation tools that collect, store, and analyze data.
These tools perform at least one of the following functions:
- Extraction: Tools can integrate with multiple platforms to extract user data. For example, data extraction tools can connect to customer relationship management or accounting systems to pull relevant data. Setting these tools to collect data on a recurring basis ensures that data is always updated.
- Transformation: Data automation tools can also clean and standardize data to make it more accurate and searchable. Tools can recognize and remove repeat entries and ensure date, text, and numerical figures follow the same format.
- Uploading: These tools then upload clean, accurate data into storage or visualization tools. This includes data warehouses, databases, or dashboards.
Benefits of a Data-driven Business
Businesses that invest in data tools and training can reap significant benefits. This includes more effective decision-making, improved customer experience, and boosted productivity. As data-driven businesses are also more likely to invest in their workforce, this increases retention and employee engagement.