Home Tech How Technology is enabling the transition from defense to offense data strategy

How Technology is enabling the transition from defense to offense data strategy

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An effective data strategy has been a must-have for every enterprise for years, certainly since the advent of big data enabled a new generation of risk assessment, business insights, and market forecasting. Without a data strategy, it’s a struggle for companies to choose the relevant datasets among the flood of big data, organize their data tools, and allocate resources for data analytics.

For a long time, defensive data strategies have dominated business data management. Using data for defense means focusing on minimizing downside risk, and encompasses concerns like compliance with data privacy regulations, adhering to financial and data governance requirements, and limiting fraud.

Data defense includes ensuring data integrity and the flow of data around the organization, and tends to be the preserve of legal, financial, compliance, and IT teams.

But as data sets, data tools, and the wider data ecosystem keep evolving, business data strategies have kept pace, leading offensive data strategies to start to overtake defensive approaches. However, business leaders agree that enterprises need a strong defensive foundation in order to achieve their offense data strategy goals.

Offensive data strategies center around using data to support business objectives, like producing customer insights, enabling managerial decision-making, and keeping ahead of market trends. As such, it tends to involve sales and marketing teams, as well as business management and product development.

While a number of different factors have all contributed to the rise of the offensive data strategy, advanced technology has definitely helped. Here are X ways that tech is helping smooth the way for offensive data strategies.

Artificial intelligence and machine learning add speed and accuracy

(AI) and machine learning (ML) techniques introduced automated tools for a cloud data warehouse, which speed up data preprocessing and help reduce the risk of manual error. Time-consuming, error-prone tasks like data deduplication and data verification are far easier and faster when using AI and ML.

While this is beneficial for both kinds of data strategies, it’s particularly crucial for data offense. Sales and marketing teams need accurate, real-time information about customer expectations and market changes, so they can forecast trends and adapt their tactics to stay ahead of the competition.

Improved visualizations support competitive decision-making

AI and ML are also part of the process enabling interactive dashboards which guide and inform managerial decision-making. These are easy to use tools with intuitive interfaces that allow all stakeholders to manipulate data visualizations and interact with the data according to their needs.

With the help of interactive dashboards, it’s easier to plot complex customer journeys, track rapid fluctuations in market trends, and carry out other data analysis that forms the basis for business decisions.

Cloud data storage opens up access to data

Cloud data storage plays a vital role in helping to break down silos and connect data in a single data repository, allowing advanced analytics tools to access all the datasets they need. By connecting data, business leaders can better assess risk and opportunity, using data to direct their decision-making for a competitive advantage.

Combining a data lake and cloud data warehouse means that the data can be updated quickly and accurately, crystallizing a single source of truth that’s accessible to all stakeholders.

Additionally, cloud data platforms can host in-place ML-powered data analytics. This way, users can train models within the same platform from wherever they are located — vital during a time of widespread remote working. It also removes the need to transfer data, making the process even more speedy and accurate.

Cloud data lakes allow for multiple versions of the truth

Defensive data strategy tends to focus on assuring a single source of truth. While that remains valuable, offensive strategies need to also support multiple versions of truth. This means that different stakeholders can access data in different ways, according to their needs, but without muddying the provenance of the data or harming data quality.

For example, both marketing teams and finance teams want to analyze advertising spend, but finance teams want to run reports showing budget allocation as soon as the payments are made, to keep track of profit and loss. Marketing teams, however, want to analyze it in light of impressions, conversions, and overall RoI, and compare that with different content and format decisions.

Cloud data lakes can keep both teams happy, preserving the trustworthiness of the original datasets while allowing different departments to run their own queries and visualizations.

Flexible data workflows allow for data transformation

The more flexible data is, the more it can be used for offensive purposes because it can be easily transformed, moved, or interpreted for specific business needs.

A more flexible data architecture, which encompasses AI, ML, cloud data storage, and cloud workflows, can feed raw data into information architecture systems such as interactive dashboards, allowing users to integrate and analyze it to find new competitive opportunities hiding between datasets.

New tech helps data strategies to play offense

Today’s new, offense data strategies are made possible not just by changing attitudes to data, but by advances in technology like AI, ML, cloud data storage, flexible data workflows, and interactive visualizations that make data processing faster, more accurate, more powerful, and more accessible, driving revenue and profit for enterprises across verticals.

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