Trends Shaping Business Data Strategy in 2023
Chandler is a seasoned business leader and people-builder, specializing in data, digital experience, and business intelligence, also built and lead an extra ordinary team in APAC to automate data pipelines, also managed transition of team and customers through acquisition by SAP.
The year 2023 will be a turning point for data teams. Organizations that have invested heavily in IT solutions will need to determine the quality of their returns to justify their acquisition. According to a report by IDC, spending growth on IT solutions have slowed from 25.3 percent in 2021 to 13.8 percent in 2022. At the same time, organizations have repositioned data teams to become the drivers of executive level decision-making to maximize business productivity and value. With data a crucial asset in unlocking new business value, organizations need to ensure that their data pipelines are robust and accurate while also controlling their tech spend. At the same time, data teams need to widen the scope of their data to ensure the business can extract better, more valuable insights and be more nimble.
Strengthening Data Stacks during Economic Instability
During this period of economic instability, many organizations will likely rationalize spending on new tech investments, including modern data stack solutions. While this trend will not halt the progress of new data projects, organizations will re-focus these projects to tackle specific objectives, such as fulfilling business use cases or increasing operational performance.
At the same time, companies are taking a deeper look at their cloud data operational costs, including warehousing and ownership. Organizations cannot simply drive down costs as it might reduce the efficacy of their data solutions. Instead, the onus is on data teams to demonstrate the data stack's capabilities to store and analyze data efficiently as well as produce valuable insights that can drive their business forward.
Data accuracy and trust have become more important as companies aim to get the right solutions that will provide the most effective outcome for their business. To that end, organizations are standardizing their data stack solutions to reduce the number of tools users need to manage. This will allow for more effective business decision-making that can maximize their returns on investments (ROIs).
Maintaining a Robust Data Security
The New Year will also see C-suite managers prioritize cybersecurity for their organization, including data security. This comes as regulators introduce stricter anti trust and privacy enforcement policies in tech companies to safeguard people's personal information. Data governance will help organizations address
These challenges by giving data teams control over how data is handled, including encryption, user access and retention policies. A good data governance strategy will not only maximize operational performance, but also shore up customers' and clients' trust.
Observ ability will also be another important trend as organizations look to enable smarter data analytics. Organizations are increasingly questioning if their data can be trusted, whether they are relevant for their use case and whether they are up-to-date.By being able to answer these questions, organizations will be able to get the best outcomes for their data projects. At the same time, organizations will face fewer regulatory challenges, increase business efficiency and reduce costs.
As organizations look to cloud native applications to enhance their operations, there will also be increased collaboration between cloud security and application security teams. This trend will unify features such as vulnerability and risk management, code scanning and threat modeling that will help both teams deliver holistic security for applications located within cloud environments.
Data’s Place in the Tech Industry
Data will play a crucial role in proving the value of business projects and their impact on stakeholders. In particular, emerging technologies like metaverse and adaptive AI can continuously stream new data points that informs organizations about customers' needs and issues. Ensuring success in these areas will require the use of a data platform that can constantly pull in new or updated data from various sources and apply them across various teams and departments. Data streaming will find its place among modern data stacks and will drive new use cases such as predicting new trends, analysing security incidents and building customer profiles.
Equally as important is the role of data teams in managing dashboards that enable data storage and analytics. With the need for more accurate insights, analysts can no longer rely on the hunt-and-peck approach that directs teams to specific sites or repositories to extract relevant data as they might miss crucial insights. The best data teams will be those willing to be flexible and search beyond what industry leaders already know so that companies can maximize their success. On the other hand, data teams who are unable to acquire the best insights may face
added budgetary pressures, which can slow down operations and reduce the amount of data organizations can utilize.
The year 2023 will be a turning point for data teams. Organizations that have invested heavily in IT solutions will need to determine the quality of their returns to justify their acquisition. According to a report by IDC, spending growth on IT solutions have slowed from 25.3 percent in 2021 to 13.8 percent in 2022. At the same time, organizations have repositioned data teams to become the drivers of executive level decision-making to maximize business productivity and value. With data a crucial asset in unlocking new business value, organizations need to ensure that their data pipelines are robust and accurate while also controlling their tech spend. At the same time, data teams need to widen the scope of their data to ensure the business can extract better, more valuable insights and be more nimble.
Strengthening Data Stacks during Economic Instability
During this period of economic instability, many organizations will likely rationalize spending on new tech investments, including modern data stack solutions. While this trend will not halt the progress of new data projects, organizations will re-focus these projects to tackle specific objectives, such as fulfilling business use cases or increasing operational performance.
Data accuracy and trust have become more important as companies aim to get the right solutions that will provide the most effective outcome for their business
At the same time, companies are taking a deeper look at their cloud data operational costs, including warehousing and ownership. Organizations cannot simply drive down costs as it might reduce the efficacy of their data solutions. Instead, the onus is on data teams to demonstrate the data stack's capabilities to store and analyze data efficiently as well as produce valuable insights that can drive their business forward.
Data accuracy and trust have become more important as companies aim to get the right solutions that will provide the most effective outcome for their business. To that end, organizations are standardizing their data stack solutions to reduce the number of tools users need to manage. This will allow for more effective business decision-making that can maximize their returns on investments (ROIs).
Maintaining a Robust Data Security
The New Year will also see C-suite managers prioritize cybersecurity for their organization, including data security. This comes as regulators introduce stricter anti trust and privacy enforcement policies in tech companies to safeguard people's personal information. Data governance will help organizations address
These challenges by giving data teams control over how data is handled, including encryption, user access and retention policies. A good data governance strategy will not only maximize operational performance, but also shore up customers' and clients' trust.
Observ ability will also be another important trend as organizations look to enable smarter data analytics. Organizations are increasingly questioning if their data can be trusted, whether they are relevant for their use case and whether they are up-to-date.By being able to answer these questions, organizations will be able to get the best outcomes for their data projects. At the same time, organizations will face fewer regulatory challenges, increase business efficiency and reduce costs.
As organizations look to cloud native applications to enhance their operations, there will also be increased collaboration between cloud security and application security teams. This trend will unify features such as vulnerability and risk management, code scanning and threat modeling that will help both teams deliver holistic security for applications located within cloud environments.
Data’s Place in the Tech Industry
Data will play a crucial role in proving the value of business projects and their impact on stakeholders. In particular, emerging technologies like metaverse and adaptive AI can continuously stream new data points that informs organizations about customers' needs and issues. Ensuring success in these areas will require the use of a data platform that can constantly pull in new or updated data from various sources and apply them across various teams and departments. Data streaming will find its place among modern data stacks and will drive new use cases such as predicting new trends, analysing security incidents and building customer profiles.
Equally as important is the role of data teams in managing dashboards that enable data storage and analytics. With the need for more accurate insights, analysts can no longer rely on the hunt-and-peck approach that directs teams to specific sites or repositories to extract relevant data as they might miss crucial insights. The best data teams will be those willing to be flexible and search beyond what industry leaders already know so that companies can maximize their success. On the other hand, data teams who are unable to acquire the best insights may face
added budgetary pressures, which can slow down operations and reduce the amount of data organizations can utilize.
