The more your business grows, the more complex your business’s Data Architecture becomes. Enterprise Data Architecture challenges abound – from the beginning and throughout the journey. Data has now become the lifeblood of any business, and business data cannot survive without a solid underlying Data Architecture. Data helps businesses to identify risks and opportunities, understand their customers better, and make informed decisions about their business operations.
However, over the years, Data Management has become a complicated activity. Data Architecture, an integral component of Data Management, defines rules and policies for data use, data access, and data storage. Without Data Architecture, businesses cannot use data. In a data-driven business environment, a business without a solid Data Architecture will not remain relevant for long.
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Data Architecture provides the foundational structure and the framework for Data Management in a business. It includes data infrastructure, data governance, data modeling, data cleansing, data quality, data integration, data security, and other components.
Top trends for modern Data Architecture in 2022 include more speed, enhanced scalability, and more flexibility. However, these 2022 wish-list items also signal more Data Architecture challenges to overcome this year. This article highlights some of those challenges and provides guidance on how to overcome them.
Challenge 1: Data Supply Chain
The data supply chain comprises Data Management, data ownership, data storage, data access, and data use at every stage. Depending on the function of the data, data can change locations many different times, thus impacting the rest of the data-related activities across an organization.
Data Management in the digital business world, as seamless as it sounds, is actually full of management headaches. As data changes hands and moves from one owner to another, the journey of data becomes complicated and hard to govern.
Challenge 2: Data Silos
Most traditional businesses preserved data privacy by holding function-specific data in departmental silos. In that scenario, data used by one department was not available or accessible by another department. However, that caused a serious problem in the advanced analytics world, where 360-degrees customer data or enterprise marketing data are everyday necessities. Companies, irrespective of their size, type, or nature of business, soon realized that to succeed in the digital age, data had to be accessible and shareable.
Then came data science, artificial intelligence (AI), and a host of related technologies that transformed businesses overnight. Today, an average business is data-centric, data-driven, and data-powered. Data is thought of as the new currency in the global economy.
In this globally competitive business world, data in every form is traded and sold. For example, 360-degrees customer data, global sales data, health care data, and insurance history data are all available with a few keystrokes.
A modern Data Architecture is designed to “eliminate data silos, combining data from all corners of the company along with external data sources.” According to The State of Data and What’s Next report, data access in real time has been a recent challenge for global businesses operating with centralized data platforms. Now, with data mesh embedded in modern data architectures, the focus is “making data available to anyone, anywhere in a company, with a focus on speed.”
Challenge 3: Lack of Data Integration
Most businesses lack proper data collection and data integration infrastructure. In any business, data is usually collected from widely disparate sources and in widely disparate types. The big data era magnified this challenge by introducing high speed, high-volume, multi-type data emanating from many different data sources.
If multi-type data is not properly integrated and then transformed into a machine-readable format, that data cannot be used for analysis. Data silos, coupled with the absence of proper data integration tools, make it difficult for business users to know which data is available, how it is being used, or who has access to it.
A very common example is a business having CRM systems and sales systems that are completely unconnected, and there is no integration platform to connect them. This problem can pose a real threat to data-driven decision-making.
Challenge 4: Data Cleansing and Preparation
In traditional data-driven businesses, data cleansing and preparation were often inadequate due to a serious lack of proper infrastructure and workforce. These were the days when data cleansing and preparation were done manually, and the absence of the right workers resulted in poor-quality data preparation, which led to incorrect insights and bad decisions.
Thanks to modern Data Architecture, now the two critical steps of data cleansing and data preparation are built into Data Architecture. The automated data cleansing and data preparation processes have not only reduced human labor but have markedly improved the data analytics process.
Challenge 5: Data Security and Governance
Data security is closely related to Data Quality. Wherever business data is not well governed, it creates not only security risks but also Data Governance risks and failures. This results in inaccurate, irrelevant, or poor-quality data that becomes totally useless for any business purpose.
Poor-quality data results in poor business decisions, which are unacceptable in this highly competitive, global business environment. In a data-driven business, daily decisions and actions depend on the quality, security, and governance of data. Without appropriate Data Governance processes in place, business data can cease to remain useful.
According to a Forbes Council Post, most businesses have failed to retrieve the full benefits of big data technology because of lack of qualified labor and appropriate analytics tools. The goal of a modern Data Architecture is to embed self-service, data analytics tools, and wide data access to help business users do their daily jobs.
Data Governance combines many different functions such as data ownership policy development, data stewardship, Data Quality control, data access control, data security assessment, and data audits. Data Governance is thus a complicated multifunction business activity that requires deep knowledge of Data Management and Data Architecture.
Challenge 6: Cost of Running an In-House Data Center
An in-house data center comprises hardware systems, software systems, the security measures in place, and the access controls. This substantially increases the cost and management burdens on businesses. Additionally, data centers must be compliant with all regulatory bodies and policies because periodic audits by third parties can mean the difference between the survival and death of a data center.
Now, thanks to the many choices on the cloud, data centers are housed on the cloud with superior data architectures. The hosted Data Architecture offers all the convenience of business data processing and Data Management without the cost burdens and in-house resource requirements. Though public cloud is highly suited for modern Data Architecture, data security and Governance issues are compelling organizations to opt for hybrid or multi-cloud options.
Challenge 7: Absence of Skilled Workers
For many years, businesses could not reap the full benefits of data-powered systems and processes because they could not afford many data scientists.
The absence of qualified Data Science workers has been sharply mitigated by modern data architectures. In a data-first business world, businesses no longer need to depend on expensive and scarce data scientists for advanced analytics and data-driven decisions.
Modern data architectures have ensured that democratic decision-making is enabled in businesses through wide extensive data access and self-service data platforms. This article highlights the Data Architecture challenges related to finding and hiring data scientists and big data analysts.
Challenge 8: The Evolving Role of the Data Architect
In 2022, data architects are considered the “big-game fish” that prospective employers are after. In today’s business environment, the data architect is a strategic staff member, enjoying a bigger paycheck and a bigger say in the business.
The role of the data architect has dramatically changed over the last few years, with Data Architecture platforms becoming fully or semi-automated. Artificial intelligence- and machine learning-powered Data Architecture solutions have transformed the data architect into a seasoned professional capable of developing “crucial architecture documents and documenting possible risks.”
Challenge 9: Data Gravity
In the hybrid or multi-cloud environments, businesses are facing a phenomenon called “data gravity,” a problem arising out of multi-platform applications, and ingesting data sets from disparate sources. However, the good news is that these Data Architecture challenges can be mitigated through distinct data storage platforms, edge computing, event-driven architectures, and using batch processing on a public cloud.
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